In this file, we presented the results from analyses of exposures
against computed epigenetic age accelerations (EAA) calculated using DNA
methylation data by different methods. For each exposure, we conducted
both primary analyses (likelihood ratio tests and generalized estimating
equations (GEE), adjusted for confounders) and sensitivity analyses
(likelihood ratio tests and/or generalized estimating equations (GEE)
limited to certain fuel users, not adjusted for confounders). In
addition to what’s included in the analysis plan, we also analyzed
ambient and urinary exposures.
1. Table with summarize info [table 1]
1.1. Description of study population (first visit for all
objects)
There are 129 visits with corresponding epigenetic ages available
among 106 female subjects. For these 106 subjects, 83 have been visited
once and 23 have been visited twice.
The following tables summarize all the information of the first visit
of these 106 subjects.
Baseline characteristics (confounders)
|
Overall
(N=106) |
| Age |
|
| Mean (SD) |
56.2 (15.0) |
| Median (IQR) |
58.0 (24.8) |
| BMI |
|
| Mean (SD) |
22.0 (3.46) |
| Median (IQR) |
21.7 (4.46) |
| factor(county) |
|
| Fuyuan |
53 (50.0%) |
| Xuanwe |
53 (50.0%) |
| factor(ses) |
|
| 0 |
53 (50.0%) |
| 1 |
53 (50.0%) |
| factor(edu) |
|
| 1 |
72 (67.9%) |
| 2 |
17 (16.0%) |
| 3 |
13 (12.3%) |
| 4 |
4 (3.8%) |
Epigenetic ages
|
Overall
(N=106) |
| DNAmAge |
|
| Mean (SD) |
56.3 (13.5) |
| Median (IQR) |
57.6 (22.0) |
| DNAmAgeHannum |
|
| Mean (SD) |
59.0 (14.4) |
| Median (IQR) |
59.9 (23.7) |
| DNAmPhenoAge |
|
| Mean (SD) |
54.7 (14.0) |
| Median (IQR) |
54.8 (21.6) |
| DNAmAgeSkinBloodClock |
|
| Mean (SD) |
55.8 (13.2) |
| Median (IQR) |
57.6 (23.5) |
| DNAmGrimAge |
|
| Mean (SD) |
55.3 (11.8) |
| Median (IQR) |
57.2 (19.4) |
| DNAmTL |
|
| Mean (SD) |
6.84 (0.329) |
| Median (IQR) |
6.85 (0.461) |
Epigenetic ages accelarations
|
Overall
(N=106) |
| AgeAccelerationResidual |
|
| Mean (SD) |
0.182 (4.62) |
| Median (IQR) |
0.213 (5.90) |
| AgeAccelerationResidualHannum |
|
| Mean (SD) |
-0.435 (4.06) |
| Median (IQR) |
-0.432 (4.79) |
| AgeAccelPheno |
|
| Mean (SD) |
-0.746 (4.45) |
| Median (IQR) |
-1.06 (5.61) |
| DNAmAgeSkinBloodClockAdjAge |
|
| Mean (SD) |
-0.000985 (3.43) |
| Median (IQR) |
0.380 (3.58) |
| AgeAccelGrim |
|
| Mean (SD) |
-0.320 (2.96) |
| Median (IQR) |
-0.913 (3.27) |
| DNAmTLAdjAge |
|
| Mean (SD) |
0.0307 (0.182) |
| Median (IQR) |
0.0347 (0.238) |
| IEAA |
|
| Mean (SD) |
0.0900 (4.34) |
| Median (IQR) |
0.303 (5.90) |
| EEAA |
|
| Mean (SD) |
-0.534 (5.24) |
| Median (IQR) |
-0.425 (6.83) |
Fuel/stove type exposures
|
Overall
(N=106) |
| factor(curFuel) |
|
| Smokeles |
13 (12.3%) |
| Smoky |
82 (77.4%) |
| Wood_and_or_Plant |
11 (10.4%) |
| factor(brthFuel) |
|
| Mix |
43 (40.6%) |
| Outside of XW/FY |
3 (2.8%) |
| Smokeles |
5 (4.7%) |
| Smoky |
45 (42.5%) |
| Wood |
10 (9.4%) |
| factor(childFuel) |
|
| Mix |
50 (47.2%) |
| Smokeles |
4 (3.8%) |
| Smoky |
43 (40.6%) |
| Wood |
9 (8.5%) |
| factor(curFuel_detail) |
|
| Plant |
4 (3.8%) |
| Smokeles |
13 (12.3%) |
| Smoky |
82 (77.4%) |
| Wood |
7 (6.6%) |
| factor(cumFuel) |
|
| Mix |
66 (62.3%) |
| Smokeles |
1 (0.9%) |
| Smoky |
37 (34.9%) |
| Wood |
2 (1.9%) |
| factor(curStove) |
|
| Firepit_and_unventilated |
16 (15.1%) |
| Mix |
14 (13.2%) |
| Portable_stove |
16 (15.1%) |
| Ventilated |
44 (41.5%) |
| Missing |
16 (15.1%) |
5MC exposures
|
Overall
(N=106) |
| cur_5mc |
|
| Mean (SD) |
8.13 (4.14) |
| Median (IQR) |
7.61 (4.48) |
| Missing |
2 (1.9%) |
| cum_5mc |
|
| Mean (SD) |
266 (149) |
| Median (IQR) |
236 (194) |
| Missing |
2 (1.9%) |
| bir_5mc |
|
| Mean (SD) |
5.14 (2.81) |
| Median (IQR) |
4.73 (5.23) |
| Missing |
2 (1.9%) |
| cur_5mc_measured |
|
| Mean (SD) |
13.7 (40.7) |
| Median (IQR) |
5.71 (5.83) |
| Missing |
62 (58.5%) |
## [1] "Pearson pair-wise correlation:"
## cur_5mc cum_5mc bir_5mc cur_5mc_measured
## cur_5mc 1.0000000 0.6947332 0.67276560 0.10959754
## cum_5mc 0.6947332 1.0000000 0.83239536 0.17329428
## bir_5mc 0.6727656 0.8323954 1.00000000 -0.05013945
## cur_5mc_measured 0.1095975 0.1732943 -0.05013945 1.00000000
## [1] "Spearman pair-wise correlation:"
## cur_5mc cum_5mc bir_5mc cur_5mc_measured
## cur_5mc 1.0000000 0.6291806 0.6239118 0.4534635
## cum_5mc 0.6291806 1.0000000 0.8206830 0.3159166
## bir_5mc 0.6239118 0.8206830 1.0000000 0.2008400
## cur_5mc_measured 0.4534635 0.3159166 0.2008400 1.0000000
Cluster-based exposures
clusCUR6
Clusters based on model-based exposure estimates at or shortly before
the visit
|
Overall
(N=106) |
| CUR6_BC_PAH6 |
|
| Mean (SD) |
0.222 (0.975) |
| Median (IQR) |
0.799 (1.21) |
| Missing |
2 (1.9%) |
| CUR6_PAH31 |
|
| Mean (SD) |
0.205 (0.977) |
| Median (IQR) |
0.433 (1.10) |
| Missing |
2 (1.9%) |
| CUR6_NkF |
|
| Mean (SD) |
-0.0581 (0.985) |
| Median (IQR) |
-0.297 (1.20) |
| Missing |
2 (1.9%) |
| CUR6_PM_RET |
|
| Mean (SD) |
-0.0120 (1.01) |
| Median (IQR) |
-0.298 (0.947) |
| Missing |
2 (1.9%) |
| CUR6_NO2 |
|
| Mean (SD) |
0.0752 (1.00) |
| Median (IQR) |
-0.0204 (1.14) |
| Missing |
2 (1.9%) |
| CUR6_SO2 |
|
| Mean (SD) |
-0.205 (0.995) |
| Median (IQR) |
-0.335 (1.16) |
| Missing |
2 (1.9%) |
clusCHLD5
Clusters based on model-based exposure estimates accrued before age
18
|
Overall
(N=106) |
| CHLD5_X7 |
|
| Mean (SD) |
-0.0684 (0.965) |
| Median (IQR) |
0.0837 (0.991) |
| Missing |
2 (1.9%) |
| CHLD5_X33 |
|
| Mean (SD) |
0.126 (0.995) |
| Median (IQR) |
-0.0314 (1.65) |
| Missing |
2 (1.9%) |
| CHLD5_NkF |
|
| Mean (SD) |
-0.0960 (0.992) |
| Median (IQR) |
-0.203 (1.36) |
| Missing |
2 (1.9%) |
| CHLD5_NO2 |
|
| Mean (SD) |
0.119 (1.02) |
| Median (IQR) |
0.196 (1.18) |
| Missing |
2 (1.9%) |
| CHLD5_SO2 |
|
| Mean (SD) |
-0.0588 (1.00) |
| Median (IQR) |
0.355 (1.57) |
| Missing |
2 (1.9%) |
clusCUM6
Clusters based on model-based lifetime exposure estimates
|
Overall
(N=106) |
| CUM6_BC_NO2_PM |
|
| Mean (SD) |
0.0305 (1.04) |
| Median (IQR) |
0.164 (1.71) |
| Missing |
2 (1.9%) |
| CUM6_PAH36 |
|
| Mean (SD) |
0.141 (0.995) |
| Median (IQR) |
0.147 (1.65) |
| Missing |
2 (1.9%) |
| CUM6_DlP |
|
| Mean (SD) |
-0.196 (1.00) |
| Median (IQR) |
-0.445 (1.70) |
| Missing |
2 (1.9%) |
| CUM6_NkF |
|
| Mean (SD) |
-0.0526 (1.02) |
| Median (IQR) |
-0.150 (1.23) |
| Missing |
2 (1.9%) |
| CUM6_RET |
|
| Mean (SD) |
-0.156 (1.01) |
| Median (IQR) |
-0.249 (1.12) |
| Missing |
2 (1.9%) |
| CUM6_SO2 |
|
| Mean (SD) |
-0.175 (1.00) |
| Median (IQR) |
-0.0667 (1.62) |
| Missing |
2 (1.9%) |
clusMEAS6
Clusters based on pollutant measurements
|
Overall
(N=106) |
| MEAS6_BC_PM_RET |
|
| Mean (SD) |
0.000103 (0.962) |
| Median [Min, Max] |
0.0484 [-2.12, 2.58] |
| Missing |
57 (53.8%) |
| MEAS6_X31 |
|
| Mean (SD) |
0.0157 (0.910) |
| Median [Min, Max] |
0.148 [-1.94, 2.13] |
| Missing |
57 (53.8%) |
| MEAS6_X5 |
|
| Mean (SD) |
0.00979 (0.975) |
| Median [Min, Max] |
-0.102 [-1.42, 1.75] |
| Missing |
57 (53.8%) |
| MEAS6_DlP |
|
| Mean (SD) |
-0.0238 (0.992) |
| Median [Min, Max] |
-0.640 [-1.02, 1.76] |
| Missing |
57 (53.8%) |
| MEAS6_NkF |
|
| Mean (SD) |
0.0443 (1.02) |
| Median [Min, Max] |
-0.489 [-1.18, 1.85] |
| Missing |
57 (53.8%) |
| MEAS6_NO2_SO2 |
|
| Mean (SD) |
0.0300 (0.989) |
| Median [Min, Max] |
-0.114 [-1.56, 2.06] |
| Missing |
57 (53.8%) |
clusURI5
Clusters based on urinary biomarkers
|
Overall
(N=106) |
| URI5_NAP_1M_2M |
|
| Mean (SD) |
0.0130 (1.01) |
| Median (IQR) |
0.0739 (1.24) |
| Missing |
13 (12.3%) |
| URI5_ACE |
|
| Mean (SD) |
-0.118 (0.997) |
| Median (IQR) |
-0.125 (1.35) |
| Missing |
13 (12.3%) |
| URI5_FLU_PHE |
|
| Mean (SD) |
-0.0444 (1.00) |
| Median (IQR) |
0.0608 (1.31) |
| Missing |
13 (12.3%) |
| URI5_PYR |
|
| Mean (SD) |
-0.0612 (1.03) |
| Median (IQR) |
0.0734 (0.925) |
| Missing |
13 (12.3%) |
| URI5_CHR |
|
| Mean (SD) |
-0.0236 (1.02) |
| Median (IQR) |
-0.0342 (0.950) |
| Missing |
13 (12.3%) |
Ambient exposures
|
Overall
(N=106) |
| bap_air |
|
| Mean (SD) |
66.3 (90.5) |
| Median (IQR) |
39.5 (55.9) |
| Missing |
3 (2.8%) |
| pm25_air |
|
| Mean (SD) |
205 (188) |
| Median (IQR) |
144 (133) |
| ANY_air |
|
| Mean (SD) |
908 (1540) |
| Median (IQR) |
486 (651) |
| Missing |
33 (31.1%) |
| BPE_air |
|
| Mean (SD) |
69.2 (93.1) |
| Median (IQR) |
40.8 (51.7) |
| Missing |
3 (2.8%) |
| BaA_air |
|
| Mean (SD) |
91.3 (153) |
| Median (IQR) |
43.7 (76.8) |
| Missing |
3 (2.8%) |
| BbF_air |
|
| Mean (SD) |
110 (151) |
| Median (IQR) |
64.7 (92.2) |
| Missing |
3 (2.8%) |
| BkF_air |
|
| Mean (SD) |
23.5 (33.2) |
| Median (IQR) |
13.2 (19.5) |
| Missing |
3 (2.8%) |
| CHR_air |
|
| Mean (SD) |
88.0 (141) |
| Median (IQR) |
48.6 (74.5) |
| Missing |
3 (2.8%) |
| DBA_air |
|
| Mean (SD) |
23.1 (35.9) |
| Median (IQR) |
11.0 (22.4) |
| Missing |
3 (2.8%) |
| FLT_air |
|
| Mean (SD) |
65.2 (146) |
| Median (IQR) |
17.5 (41.0) |
| Missing |
3 (2.8%) |
| FLU_air |
|
| Mean (SD) |
441 (691) |
| Median (IQR) |
250 (260) |
| Missing |
33 (31.1%) |
| IPY_air |
|
| Mean (SD) |
40.9 (49.7) |
| Median (IQR) |
27.1 (33.5) |
| Missing |
3 (2.8%) |
| NAP_air |
|
| Mean (SD) |
5340 (8070) |
| Median (IQR) |
3020 (3540) |
| Missing |
33 (31.1%) |
| PHE_air |
|
| Mean (SD) |
675 (1080) |
| Median (IQR) |
351 (533) |
| Missing |
33 (31.1%) |
| PYR_air |
|
| Mean (SD) |
71.3 (149) |
| Median (IQR) |
21.3 (50.6) |
| Missing |
3 (2.8%) |
Urinary biomarkers
|
Overall
(N=106) |
| Benzanthracene_Chrysene_urine |
|
| Mean (SD) |
0.976 (3.49) |
| Median (IQR) |
0.389 (0.465) |
| Missing |
2 (1.9%) |
| Naphthalene_urine |
|
| Mean (SD) |
247 (755) |
| Median (IQR) |
113 (112) |
| Methylnaphthalene_2_urine |
|
| Mean (SD) |
48.7 (64.7) |
| Median (IQR) |
29.8 (29.4) |
| Missing |
8 (7.5%) |
| Methylnaphthalene_1_urine |
|
| Mean (SD) |
21.0 (26.4) |
| Median (IQR) |
12.0 (18.7) |
| Missing |
3 (2.8%) |
| Acenaphthene_urine |
|
| Mean (SD) |
7.76 (11.4) |
| Median (IQR) |
3.34 (5.84) |
| Phenanthrene_Anthracene_urine |
|
| Mean (SD) |
216 (296) |
| Median (IQR) |
116 (191) |
| Fluoranthene_urine |
|
| Mean (SD) |
21.8 (25.1) |
| Median (IQR) |
16.5 (17.5) |
| Pyrene_urine |
|
| Mean (SD) |
0.744 (0.622) |
| Median (IQR) |
0.552 (0.423) |
| Missing |
15 (14.2%) |
2.1. Current fuel type
Primary analysis
GEE (with confounders, Smokeless ref)
The numbers of observations with each current fuel type:
##
## Smokeles Smoky Wood_and_or_Plant
## 18 98 13
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between current fuel type and each
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smoky}) + \beta_2 *
I(\text{Wood_and_or_Plant}) \\
& + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 *
edu + \epsilon
\end{aligned}
\]
Results:
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## The estimated average EAA differences of Smoky fuel type to Smokeless fuel yupes:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.6373 1.6475 -4.8665 1.5918 0.320311
## AgeAccelerationResidualHannum -0.9486 1.2628 -3.4236 1.5264 0.452513
## AgeAccelPheno 1.1951 1.0876 -0.9365 3.3268 0.271816
## DNAmAgeSkinBloodClockAdjAge 0.6653 1.1116 -1.5134 2.8439 0.549497
## AgeAccelGrim 1.8408 0.6363 0.5936 3.0880 0.003818
## DNAmTLAdjAge -0.0405 0.0550 -0.1484 0.0674 0.462124
## IEAA -1.2847 1.4695 -4.1649 1.5956 0.381995
## EEAA -1.0318 1.4383 -3.8509 1.7874 0.473174
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
## The estimated average EAA differences of Wood and/or Plant fuel type to Smokeless fuel yupes:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2807 1.8786 -3.4013 3.9628 0.881211
## AgeAccelerationResidualHannum -1.5199 1.4575 -4.3766 1.3369 0.297047
## AgeAccelPheno 1.4072 1.5327 -1.5969 4.4112 0.358562
## DNAmAgeSkinBloodClockAdjAge 0.4910 1.6638 -2.7701 3.7521 0.767910
## AgeAccelGrim 2.3561 1.0997 0.2008 4.5114 0.032148
## DNAmTLAdjAge -0.0029 0.0773 -0.1543 0.1486 0.970307
## IEAA 0.9215 1.5787 -2.1729 4.0158 0.559436
## EEAA -1.6640 1.8379 -5.2664 1.9383 0.365260
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (with confounders, Other ref)
The numbers of observations with each current fuel type:
##
## Other Smoky
## 31 98
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between current fuel type and each
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smoky}) \\
& + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 *
edu + \epsilon
\end{aligned}
\]
Results:
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## The estimated average EAA differences of Smoky fuel type to Other fuel types:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.7835 0.9675 -3.6798 0.1128 0.065268
## AgeAccelerationResidualHannum -0.0869 0.7443 -1.5457 1.3719 0.907055
## AgeAccelPheno 0.4712 0.9318 -1.3551 2.2975 0.613081
## DNAmAgeSkinBloodClockAdjAge 0.4084 0.9302 -1.4148 2.2315 0.660633
## AgeAccelGrim 0.5676 0.6339 -0.6749 1.8102 0.370563
## DNAmTLAdjAge -0.0389 0.0448 -0.1268 0.0489 0.385016
## IEAA -1.7512 0.9041 -3.5233 0.0209 0.052758
## EEAA -0.1070 0.9926 -2.0526 1.8386 0.914149
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (with confounders, Wood_and_or_Plant ref)
The numbers of observations with each current fuel type:
##
## Smokeles Smoky Wood_and_or_Plant
## 18 98 13
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between current fuel type and each
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smoky}) + \beta_2 *
I(\text{Wood_and_or_Plant}) \\
& + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 *
edu + \epsilon
\end{aligned}
\]
Results:
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
GEE (with confounders, Smokeless_PureWood ref)
The numbers of observations with each current fuel type:
##
## Plant Smokeles Smoky Wood
## 4 18 98 9
##
## Mix Smokeles Smoky Wood
## Plant 3 0 0 1
## Smokeles 12 0 4 2
## Smoky 43 5 46 4
## Wood 5 0 0 4
##
## Mix Smokeles Smoky Wood
## Plant 4 0 0 0
## Smokeles 17 0 1 0
## Smoky 56 1 41 0
## Wood 7 0 0 2
##
## Plant Smokeless_PureWood Smoky Wood
## 4 22 98 5
Results:
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
2.2. Birth fuel type
Primary analysis
GEE (with confounders, Smokeless ref)
The numbers of observations with each birth fuel type:
##
## Mix Outside of XW/FY Smokeles Smoky
## 54 3 7 52
## Wood
## 13
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated average EAA differences of Smoky fuel type to Smokeless fuel yupes:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.3299 1.0723 -2.4316 1.7717 0.758327
## AgeAccelerationResidualHannum -0.4847 1.6654 -3.7489 2.7795 0.771004
## AgeAccelPheno 2.3369 2.3508 -2.2706 6.9445 0.320173
## DNAmAgeSkinBloodClockAdjAge -0.6915 0.9741 -2.6007 1.2177 0.477779
## AgeAccelGrim 3.7919 1.1355 1.5663 6.0175 0.000840
## DNAmTLAdjAge -0.0672 0.0684 -0.2013 0.0670 0.326254
## IEAA -0.0846 1.7039 -3.4242 3.2549 0.960379
## EEAA 0.3516 2.0493 -3.6650 4.3681 0.863777
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
## The estimated average EAA differences of Wood fuel type to Smokeless fuel yupes:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.8208 1.8879 -1.8794 5.5210 0.334810
## AgeAccelerationResidualHannum 0.2055 1.8060 -3.3342 3.7452 0.909425
## AgeAccelPheno 2.6279 2.7322 -2.7272 7.9830 0.336139
## DNAmAgeSkinBloodClockAdjAge -0.8605 1.6969 -4.1865 2.4655 0.612091
## AgeAccelGrim 3.6378 1.1990 1.2878 5.9877 0.002412
## DNAmTLAdjAge -0.0638 0.0859 -0.2321 0.1045 0.457558
## IEAA 2.7412 2.0551 -1.2868 6.7693 0.182251
## EEAA 1.0126 2.3544 -3.6020 5.6272 0.667138
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
## The estimated average EAA differences of Mix fuel type to Smokeless fuel yupes:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1613 1.1556 -2.4263 2.1037 0.888990
## AgeAccelerationResidualHannum -1.2901 1.5846 -4.3958 1.8157 0.415564
## AgeAccelPheno 1.2005 2.2414 -3.1925 5.5936 0.592219
## DNAmAgeSkinBloodClockAdjAge -1.5172 0.9335 -3.3468 0.3124 0.104089
## AgeAccelGrim 2.0349 1.1102 -0.1411 4.2108 0.066811
## DNAmTLAdjAge -0.0064 0.0700 -0.1436 0.1307 0.926562
## IEAA 0.4729 1.7042 -2.8673 3.8130 0.781407
## EEAA -0.6619 1.9396 -4.4635 3.1397 0.732915
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (with confounders, Mix ref)
The numbers of observations with each birth fuel type:
##
## Mix Outside of XW/FY Smokeles Smoky
## 54 3 7 52
## Wood
## 13
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated average EAA differences of Smoky fuel type to Smokeless fuel yupes:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.3299 1.0723 -2.4316 1.7717 0.758327
## AgeAccelerationResidualHannum -0.4847 1.6654 -3.7489 2.7795 0.771004
## AgeAccelPheno 2.3369 2.3508 -2.2706 6.9445 0.320173
## DNAmAgeSkinBloodClockAdjAge -0.6915 0.9741 -2.6007 1.2177 0.477779
## AgeAccelGrim 3.7919 1.1355 1.5663 6.0175 0.000840
## DNAmTLAdjAge -0.0672 0.0684 -0.2013 0.0670 0.326254
## IEAA -0.0846 1.7039 -3.4242 3.2549 0.960379
## EEAA 0.3516 2.0493 -3.6650 4.3681 0.863777
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
## The estimated average EAA differences of Wood fuel type to Smokeless fuel yupes:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.8208 1.8879 -1.8794 5.5210 0.334810
## AgeAccelerationResidualHannum 0.2055 1.8060 -3.3342 3.7452 0.909425
## AgeAccelPheno 2.6279 2.7322 -2.7272 7.9830 0.336139
## DNAmAgeSkinBloodClockAdjAge -0.8605 1.6969 -4.1865 2.4655 0.612091
## AgeAccelGrim 3.6378 1.1990 1.2878 5.9877 0.002412
## DNAmTLAdjAge -0.0638 0.0859 -0.2321 0.1045 0.457558
## IEAA 2.7412 2.0551 -1.2868 6.7693 0.182251
## EEAA 1.0126 2.3544 -3.6020 5.6272 0.667138
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
## The estimated average EAA differences of Mix fuel type to Smokeless fuel yupes:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1613 1.1556 -2.4263 2.1037 0.888990
## AgeAccelerationResidualHannum -1.2901 1.5846 -4.3958 1.8157 0.415564
## AgeAccelPheno 1.2005 2.2414 -3.1925 5.5936 0.592219
## DNAmAgeSkinBloodClockAdjAge -1.5172 0.9335 -3.3468 0.3124 0.104089
## AgeAccelGrim 2.0349 1.1102 -0.1411 4.2108 0.066811
## DNAmTLAdjAge -0.0064 0.0700 -0.1436 0.1307 0.926562
## IEAA 0.4729 1.7042 -2.8673 3.8130 0.781407
## EEAA -0.6619 1.9396 -4.4635 3.1397 0.732915
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (with confounders, Other ref)
The numbers of observations with each current fuel type:
##
## Other Smoky
## 74 52
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between current fuel type and each
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smoky}) \\
& + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 *
edu + \epsilon
\end{aligned}
\]
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated average EAA differences of Smoky fuel type to Other fuel types:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.5082 0.8558 -2.1856 1.1692 0.552615
## AgeAccelerationResidualHannum 0.4622 0.8025 -1.1107 2.0351 0.564657
## AgeAccelPheno 0.9806 0.9042 -0.7916 2.7528 0.278140
## DNAmAgeSkinBloodClockAdjAge 0.5969 0.7270 -0.8280 2.0217 0.411628
## AgeAccelGrim 1.6159 0.5592 0.5200 2.7119 0.003853
## DNAmTLAdjAge -0.0514 0.0339 -0.1179 0.0150 0.128911
## IEAA -0.9105 0.8021 -2.4825 0.6616 0.256307
## EEAA 0.6851 1.0383 -1.3499 2.7200 0.509366
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
2.3. Childhood fuel type
Primary analysis
GEE (with confounders, Smokeless ref)
The numbers of observations with each current fuel type:
##
## Mix Smokeles Smoky Wood
## 63 5 50 11
In this section, we perform the generalized estimating equations
(GEE) to evaluate the association between current fuel type and the Grim
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smoky}) + \beta_2 *
I(\text{Wood}) + \beta_3 * I(\text{Mix}) \\
& + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 *
edu + \epsilon
\end{aligned}
\]
Results:
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## The estimated average EAA difference of childhood fuel type, smoky compared to smokeless coal:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0956 1.2364 -2.5189 2.3277 0.938358
## AgeAccelerationResidualHannum 0.3763 1.8405 -3.2310 3.9836 0.838002
## AgeAccelPheno 3.7272 2.6451 -1.4572 8.9116 0.158810
## DNAmAgeSkinBloodClockAdjAge -0.4809 1.1077 -2.6520 1.6902 0.664177
## AgeAccelGrim 4.1352 1.2796 1.6272 6.6432 0.001231
## DNAmTLAdjAge -0.0420 0.0808 -0.2003 0.1163 0.603409
## IEAA 0.5886 2.0639 -3.4567 4.6339 0.775517
## EEAA 1.2117 2.3042 -3.3046 5.7279 0.599001
## sig_level EAAs
## AgeAccelerationResidual > 0.05 Horvath EAA
## AgeAccelerationResidualHannum > 0.05 Hannum EAA
## AgeAccelPheno > 0.05 PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge > 0.05 Skin&Blood EAA
## AgeAccelGrim <= 0.01 GrimAge EAA
## DNAmTLAdjAge > 0.05 DNAmTL
## IEAA > 0.05 IEAA
## EEAA > 0.05 EEAA
## The estimated average EAA difference of childhood fuel type, wood compared to smokeless coal:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3260 1.6499 -2.9078 3.5597 0.843384
## AgeAccelerationResidualHannum -0.0761 1.7618 -3.5293 3.3772 0.965566
## AgeAccelPheno 2.9529 2.9563 -2.8413 8.7472 0.317852
## DNAmAgeSkinBloodClockAdjAge -1.5960 1.7026 -4.9331 1.7412 0.348569
## AgeAccelGrim 3.8507 1.3325 1.2391 6.4624 0.003853
## DNAmTLAdjAge -0.0173 0.0954 -0.2043 0.1696 0.855744
## IEAA 2.3857 2.3225 -2.1663 6.9377 0.304310
## EEAA 0.4301 2.3257 -4.1283 4.9885 0.853289
## sig_level EAAs
## AgeAccelerationResidual > 0.05 Horvath EAA
## AgeAccelerationResidualHannum > 0.05 Hannum EAA
## AgeAccelPheno > 0.05 PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge > 0.05 Skin&Blood EAA
## AgeAccelGrim <= 0.01 GrimAge EAA
## DNAmTLAdjAge > 0.05 DNAmTL
## IEAA > 0.05 IEAA
## EEAA > 0.05 EEAA
## The estimated average EAA difference of childhood fuel type, mix compared to smokeless coal:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3827 1.2369 -2.0416 2.8070 0.757026
## AgeAccelerationResidualHannum -0.2240 1.7117 -3.5790 3.1309 0.895875
## AgeAccelPheno 2.8842 2.5272 -2.0692 7.8375 0.253765
## DNAmAgeSkinBloodClockAdjAge -0.7275 1.0250 -2.7364 1.2815 0.477860
## AgeAccelGrim 2.6272 1.2363 0.2041 5.0504 0.033581
## DNAmTLAdjAge 0.0058 0.0817 -0.1543 0.1659 0.943149
## IEAA 1.4006 2.0535 -2.6243 5.4254 0.495205
## EEAA 0.4420 2.1481 -3.7683 4.6522 0.836985
## sig_level EAAs
## AgeAccelerationResidual > 0.05 Horvath EAA
## AgeAccelerationResidualHannum > 0.05 Hannum EAA
## AgeAccelPheno > 0.05 PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge > 0.05 Skin&Blood EAA
## AgeAccelGrim <= 0.05 GrimAge EAA
## DNAmTLAdjAge > 0.05 DNAmTL
## IEAA > 0.05 IEAA
## EEAA > 0.05 EEAA
GEE (with confounders, Other ref)
The numbers of observations with each current fuel type:
##
## Other Smoky
## 79 50
In this section, we perform the generalized estimating equations
(GEE) to evaluate the association between current fuel type and the Grim
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smoky}) \\
& + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 *
edu + \epsilon
\end{aligned}
\]
Results:
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## The estimated average EAA difference of childhood fuel type, smoky compared to other coal:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.4469 0.8603 -2.1332 1.2393 0.603402
## AgeAccelerationResidualHannum 0.5627 0.8193 -1.0431 2.1684 0.492205
## AgeAccelPheno 0.9966 0.9137 -0.7943 2.7875 0.275421
## DNAmAgeSkinBloodClockAdjAge 0.3349 0.7345 -1.1048 1.7745 0.648464
## AgeAccelGrim 1.4687 0.5826 0.3267 2.6106 0.011708
## DNAmTLAdjAge -0.0440 0.0339 -0.1104 0.0225 0.194987
## IEAA -0.8683 0.8044 -2.4449 0.7083 0.280380
## EEAA 0.7996 1.0723 -1.3021 2.9013 0.455848
## sig_level EAAs
## AgeAccelerationResidual > 0.05 Horvath EAA
## AgeAccelerationResidualHannum > 0.05 Hannum EAA
## AgeAccelPheno > 0.05 PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge > 0.05 Skin&Blood EAA
## AgeAccelGrim <= 0.05 GrimAge EAA
## DNAmTLAdjAge > 0.05 DNAmTL
## IEAA > 0.05 IEAA
## EEAA > 0.05 EEAA
GEE (with confounders, Smokeless_Wood ref)
The numbers of observations with each current fuel type:
##
## Mix Smokeless_Wood Smoky
## 63 16 50
Results:
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## The estimated average EAA difference of childhood fuel type, smoky compared to Smokeless_Wood coal:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.3267 1.1194 -2.5208 1.8674 0.770414
## AgeAccelerationResidualHannum 0.4301 1.0198 -1.5688 2.4290 0.673226
## AgeAccelPheno 1.6265 1.5645 -1.4398 4.6929 0.298494
## DNAmAgeSkinBloodClockAdjAge 0.6530 1.2501 -1.7972 3.1031 0.601441
## AgeAccelGrim 1.3857 0.9125 -0.4029 3.1742 0.128889
## DNAmTLAdjAge -0.0295 0.0516 -0.1307 0.0716 0.567449
## IEAA -1.0995 1.3006 -3.6486 1.4496 0.397870
## EEAA 0.9062 1.3723 -1.7836 3.5959 0.509042
## sig_level EAAs
## AgeAccelerationResidual > 0.05 Horvath EAA
## AgeAccelerationResidualHannum > 0.05 Hannum EAA
## AgeAccelPheno > 0.05 PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge > 0.05 Skin&Blood EAA
## AgeAccelGrim > 0.05 GrimAge EAA
## DNAmTLAdjAge > 0.05 DNAmTL
## IEAA > 0.05 IEAA
## EEAA > 0.05 EEAA
GEE (with confounders, Mix ref)
The numbers of observations with each current fuel type:
##
## Mix Smoky Smokeless_Wood
## 63 50 16
Results:
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## [1] "Fitting with 129 observations."
## The estimated average EAA difference of childhood fuel type, smoky compared to Mix coal:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.4782 0.9337 -2.3083 1.3518 0.608513
## AgeAccelerationResidualHannum 0.6000 0.8620 -1.0895 2.2895 0.486387
## AgeAccelPheno 0.8356 0.8968 -0.9222 2.5933 0.351491
## DNAmAgeSkinBloodClockAdjAge 0.2488 0.7302 -1.1823 1.6799 0.733289
## AgeAccelGrim 1.4909 0.6087 0.2978 2.6839 0.014313
## DNAmTLAdjAge -0.0477 0.0366 -0.1193 0.0240 0.192351
## IEAA -0.8089 0.8296 -2.4349 0.8172 0.329590
## EEAA 0.7700 1.1257 -1.4364 2.9765 0.493959
## sig_level EAAs
## AgeAccelerationResidual > 0.05 Horvath EAA
## AgeAccelerationResidualHannum > 0.05 Hannum EAA
## AgeAccelPheno > 0.05 PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge > 0.05 Skin&Blood EAA
## AgeAccelGrim <= 0.05 GrimAge EAA
## DNAmTLAdjAge > 0.05 DNAmTL
## IEAA > 0.05 IEAA
## EEAA > 0.05 EEAA
## The estimated average EAA difference of childhood fuel type, smokeless_wood compared to Mix coal:
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1516 1.1333 -2.3729 2.0698 0.893618
## AgeAccelerationResidualHannum 0.1699 0.9034 -1.6008 1.9406 0.850825
## AgeAccelPheno -0.7910 1.3899 -3.5152 1.9333 0.569306
## DNAmAgeSkinBloodClockAdjAge -0.4042 1.1346 -2.6280 1.8197 0.721688
## AgeAccelGrim 0.1052 0.8785 -1.6167 1.8271 0.904672
## DNAmTLAdjAge -0.0182 0.0532 -0.1224 0.0861 0.732849
## IEAA 0.2907 1.2343 -2.1285 2.7099 0.813819
## EEAA -0.1362 1.2208 -2.5290 2.2567 0.911200
## sig_level EAAs
## AgeAccelerationResidual > 0.05 Horvath EAA
## AgeAccelerationResidualHannum > 0.05 Hannum EAA
## AgeAccelPheno > 0.05 PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge > 0.05 Skin&Blood EAA
## AgeAccelGrim > 0.05 GrimAge EAA
## DNAmTLAdjAge > 0.05 DNAmTL
## IEAA > 0.05 IEAA
## EEAA > 0.05 EEAA
2.4. Cumulative lifetime fuel type
Primary analysis
GEE (with confounders, Mix ref)
##
## Mix Smokeles Smoky Wood
## 84 1 42 2
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between cumulative fuel type and each
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smoky}) + \beta_2 *
I(\text{Wood}) \\
& + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 *
edu + \epsilon
\end{aligned}
\]
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated average EAA difference of cumulative fuel type, smoky compared to mix coal:
## coefficient std ci_lower ci_upper
## AgeAccelerationResidual -0.74445338 0.88090099 -2.4710193 0.982112569
## AgeAccelerationResidualHannum 0.88211138 0.82632185 -0.7374794 2.501702207
## AgeAccelPheno 1.17536634 0.93444306 -0.6561421 3.006874731
## DNAmAgeSkinBloodClockAdjAge 0.18370808 0.73407898 -1.2550867 1.622502873
## AgeAccelGrim 1.53863773 0.59085841 0.3805552 2.696720213
## DNAmTLAdjAge -0.06080288 0.03302861 -0.1255390 0.003933192
## IEAA -1.21766970 0.83395715 -2.8522257 0.416886310
## EEAA 1.18384533 1.07326512 -0.9197543 3.287444968
## p_val sig_level NA EAAs
## AgeAccelerationResidual 0.398052506 > 0.05 -0.74445338 Horvath EAA
## AgeAccelerationResidualHannum 0.285739119 > 0.05 0.88211138 Hannum EAA
## AgeAccelPheno 0.208454860 > 0.05 1.17536634 PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge 0.802388950 > 0.05 0.18370808 Skin&Blood EAA
## AgeAccelGrim 0.009212345 <= 0.01 1.53863773 GrimAge EAA
## DNAmTLAdjAge 0.065633927 > 0.05 -0.06080288 DNAmTL
## IEAA 0.144259671 > 0.05 -1.21766970 IEAA
## EEAA 0.270013449 > 0.05 1.18384533 EEAA
2.0. All plots together
Current: smoky vs smokeless Birth: smoky vs mix Childhood: smoky vs
mix Cumulative: smoky vs mix
3.1. Clusters based on model-based exposure estimates at or shortly
before the visit (clusCUR6)
The file “LEX_clusCUR6.csv” has information on current pollutant
exposures, obtained for the 2 years preceding the visit. To reduce
multi-collinearity between exposures, exposure prototypes were derived
based on hierarchical cluster analysis in combination followed by
principal components analysis. These estimates are available for 6
different prototypes (cluster variables) for a total of 161 subjects and
211 visits. The prototypes are labelled as:
CUR6_BC_PAH6 – Black carbon (BC) and 6 PAHs
CUR6_PAH31 – a large cluster of 31 PAHs
CUR6_NkF – NkF only
CUR6_PM_RET – Particulate matter (PM) and retene
CUR6_NO2 – NO2 only
CUR6_SO2 – SO2 only
Summary the exposure estimates:
|
Smokeles
(N=18) |
Smoky
(N=98) |
Wood_and_or_Plant
(N=13) |
Overall
(N=129) |
| CUR6_BC_PAH6 |
|
|
|
|
| Mean (SD) |
-1.07 (0.524) |
0.408 (0.929) |
0.150 (0.821) |
0.194 (1.00) |
| Median (IQR) |
-1.25 (0.671) |
0.809 (1.38) |
0.587 (0.984) |
0.799 (1.36) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUR6_PAH31 |
|
|
|
|
| Mean (SD) |
-0.837 (0.949) |
0.314 (0.951) |
0.550 (0.626) |
0.192 (1.00) |
| Median (IQR) |
-1.19 (1.50) |
0.492 (0.715) |
0.811 (0.235) |
0.437 (1.09) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUR6_NkF |
|
|
|
|
| Mean (SD) |
-0.112 (1.16) |
-0.104 (0.943) |
0.126 (1.25) |
-0.0817 (1.00) |
| Median (IQR) |
0.0783 (0.481) |
-0.453 (0.788) |
0.671 (0.733) |
-0.302 (1.20) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUR6_PM_RET |
|
|
|
|
| Mean (SD) |
-0.186 (0.814) |
-0.243 (0.757) |
1.68 (1.20) |
-0.0374 (1.00) |
| Median (IQR) |
-0.0248 (1.14) |
-0.336 (0.713) |
2.52 (1.56) |
-0.316 (0.929) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUR6_NO2 |
|
|
|
|
| Mean (SD) |
0.830 (0.810) |
-0.0349 (0.995) |
0.429 (0.844) |
0.123 (1.00) |
| Median (IQR) |
0.921 (1.05) |
-0.186 (0.807) |
0.283 (1.30) |
0.0189 (1.22) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUR6_SO2 |
|
|
|
|
| Mean (SD) |
0.978 (0.927) |
-0.263 (0.890) |
-0.717 (0.924) |
-0.152 (1.00) |
| Median (IQR) |
1.54 (1.47) |
-0.335 (0.929) |
-1.11 (1.27) |
-0.335 (1.16) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
Primary analysis
GEE (with confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CUR6_BC_PAH6
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.4307 0.5443 -1.4975 0.6361 0.428773
## AgeAccelerationResidualHannum -0.3212 0.4251 -1.1543 0.5120 0.449892
## AgeAccelPheno 0.1672 0.4488 -0.7125 1.0470 0.709441
## DNAmAgeSkinBloodClockAdjAge -0.1038 0.3513 -0.7924 0.5848 0.767706
## AgeAccelGrim 0.7165 0.2606 0.2058 1.2272 0.005961
## DNAmTLAdjAge 0.0194 0.0204 -0.0207 0.0594 0.343834
## IEAA -0.1703 0.4654 -1.0826 0.7419 0.714381
## EEAA -0.6193 0.5361 -1.6701 0.4314 0.247990
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PAH31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2938 0.4854 -0.6575 1.2451 0.544985
## AgeAccelerationResidualHannum -0.1364 0.4258 -0.9710 0.6982 0.748691
## AgeAccelPheno 0.3789 0.4532 -0.5094 1.2673 0.403109
## DNAmAgeSkinBloodClockAdjAge 0.2912 0.3253 -0.3463 0.9287 0.370677
## AgeAccelGrim 0.7727 0.2128 0.3556 1.1899 0.000283
## DNAmTLAdjAge -0.0096 0.0146 -0.0383 0.0190 0.508961
## IEAA 0.2203 0.4979 -0.7556 1.1961 0.658218
## EEAA -0.2428 0.5288 -1.2792 0.7935 0.646052
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1404 0.5002 -1.1208 0.8399 0.778906
## AgeAccelerationResidualHannum -0.1953 0.4532 -1.0836 0.6930 0.666549
## AgeAccelPheno -0.4723 0.4258 -1.3068 0.3622 0.267345
## DNAmAgeSkinBloodClockAdjAge -0.0944 0.3715 -0.8226 0.6337 0.799319
## AgeAccelGrim 0.2733 0.2605 -0.2373 0.7840 0.294155
## DNAmTLAdjAge -0.0317 0.0185 -0.0680 0.0046 0.086998
## IEAA -0.1569 0.3884 -0.9182 0.6044 0.686211
## EEAA -0.2215 0.5931 -1.3839 0.9409 0.708790
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2338 0.4104 -0.5706 1.0382 0.568874
## AgeAccelerationResidualHannum -0.4395 0.3548 -1.1349 0.2558 0.215383
## AgeAccelPheno -0.3914 0.5335 -1.4370 0.6542 0.463152
## DNAmAgeSkinBloodClockAdjAge -0.3841 0.4184 -1.2041 0.4360 0.358642
## AgeAccelGrim 0.4076 0.3192 -0.2182 1.0333 0.201731
## DNAmTLAdjAge 0.0050 0.0201 -0.0345 0.0445 0.803199
## IEAA 0.3535 0.3680 -0.3679 1.0749 0.336819
## EEAA -0.5652 0.4979 -1.5410 0.4107 0.256345
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.6309 0.4625 -0.2755 1.5374 0.172479
## AgeAccelerationResidualHannum -0.1797 0.4010 -0.9656 0.6061 0.653947
## AgeAccelPheno 0.0844 0.5177 -0.9303 1.0991 0.870429
## DNAmAgeSkinBloodClockAdjAge 0.2793 0.3586 -0.4235 0.9822 0.435994
## AgeAccelGrim -0.2042 0.3007 -0.7936 0.3852 0.497130
## DNAmTLAdjAge 0.0243 0.0187 -0.0123 0.0608 0.193747
## IEAA 0.4405 0.4044 -0.3522 1.2332 0.276061
## EEAA -0.2357 0.5743 -1.3613 0.8900 0.681542
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2389 0.4305 -1.0826 0.6049 0.579009
## AgeAccelerationResidualHannum -0.3101 0.4580 -1.2078 0.5875 0.498292
## AgeAccelPheno -0.6581 0.5276 -1.6922 0.3759 0.212236
## DNAmAgeSkinBloodClockAdjAge -0.3867 0.3944 -1.1597 0.3862 0.326784
## AgeAccelGrim -0.5467 0.2722 -1.0802 -0.0132 0.044577
## DNAmTLAdjAge 0.0016 0.0178 -0.0334 0.0365 0.930686
## IEAA -0.4236 0.3963 -1.2003 0.3531 0.285062
## EEAA -0.4239 0.5390 -1.4804 0.6325 0.431545
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (with confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * BC\_PAH6 + \beta_2 PAH31 + \beta_3 NkF
+ \beta_4 PM\_RET + \beta_5 NO2 + \beta_6 SO2 \\
& + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_10 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CUR6_BC_PAH6
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.4505 0.9061 -3.2264 0.3255 0.109419
## AgeAccelerationResidualHannum -0.9124 0.6740 -2.2336 0.4087 0.175839
## AgeAccelPheno -0.9697 0.5986 -2.1431 0.2036 0.105259
## DNAmAgeSkinBloodClockAdjAge -0.9045 0.6810 -2.2393 0.4304 0.184149
## AgeAccelGrim 0.3997 0.4343 -0.4515 1.2509 0.357409
## DNAmTLAdjAge 0.0275 0.0300 -0.0313 0.0862 0.359172
## IEAA -0.9304 0.6458 -2.1961 0.3353 0.149663
## EEAA -1.4951 0.8817 -3.2233 0.2330 0.089932
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PAH31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.1155 0.7647 -0.3832 2.6142 0.144612
## AgeAccelerationResidualHannum 0.6603 0.6977 -0.7073 2.0278 0.343980
## AgeAccelPheno 1.4704 0.6551 0.1863 2.7544 0.024806
## DNAmAgeSkinBloodClockAdjAge 1.2326 0.5929 0.0706 2.3947 0.037609
## AgeAccelGrim 0.5062 0.4036 -0.2848 1.2972 0.209746
## DNAmTLAdjAge -0.0256 0.0254 -0.0755 0.0242 0.313059
## IEAA 0.5908 0.7313 -0.8426 2.0242 0.419189
## EEAA 0.9199 0.8837 -0.8120 2.6519 0.297860
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.8084 0.7397 -2.2582 0.6415 0.274474
## AgeAccelerationResidualHannum -0.2795 0.6600 -1.5731 1.0140 0.671907
## AgeAccelPheno -0.6750 0.5340 -1.7217 0.3717 0.206245
## DNAmAgeSkinBloodClockAdjAge -0.2120 0.6149 -1.4173 0.9933 0.730264
## AgeAccelGrim 0.2890 0.3895 -0.4744 1.0524 0.458054
## DNAmTLAdjAge -0.0287 0.0214 -0.0706 0.0133 0.180480
## IEAA -0.6007 0.4939 -1.5688 0.3674 0.223939
## EEAA -0.4269 0.8260 -2.0458 1.1920 0.605281
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0708 0.6016 -1.2499 1.1082 0.906278
## AgeAccelerationResidualHannum -0.6669 0.5328 -1.7112 0.3775 0.210748
## AgeAccelPheno -1.0635 0.6016 -2.2425 0.1156 0.077087
## DNAmAgeSkinBloodClockAdjAge -1.1116 0.5703 -2.2294 0.0062 0.051290
## AgeAccelGrim -0.0852 0.4022 -0.8735 0.7031 0.832250
## DNAmTLAdjAge 0.0228 0.0249 -0.0261 0.0716 0.360845
## IEAA 0.2071 0.5888 -0.9469 1.3612 0.725008
## EEAA -0.8381 0.6981 -2.2064 0.5302 0.229941
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.5926 0.4852 -0.3584 1.5436 0.221945
## AgeAccelerationResidualHannum 0.0024 0.4475 -0.8746 0.8794 0.995708
## AgeAccelPheno 0.3679 0.5322 -0.6752 1.4111 0.489355
## DNAmAgeSkinBloodClockAdjAge 0.5410 0.3960 -0.2351 1.3171 0.171881
## AgeAccelGrim -0.1738 0.3197 -0.8005 0.4529 0.586690
## DNAmTLAdjAge 0.0236 0.0191 -0.0139 0.0610 0.217968
## IEAA 0.4176 0.4263 -0.4179 1.2531 0.327258
## EEAA -0.0089 0.5901 -1.1655 1.1476 0.987912
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.6552 0.5720 -1.7764 0.4659 0.251988
## AgeAccelerationResidualHannum -0.6911 0.5273 -1.7246 0.3424 0.189971
## AgeAccelPheno -0.9978 0.4813 -1.9410 -0.0545 0.038149
## DNAmAgeSkinBloodClockAdjAge -0.8694 0.4659 -1.7826 0.0438 0.062054
## AgeAccelGrim -0.3330 0.2572 -0.8371 0.1711 0.195400
## DNAmTLAdjAge 0.0139 0.0191 -0.0236 0.0514 0.468569
## IEAA -0.6415 0.5023 -1.6260 0.3429 0.201503
## EEAA -1.0196 0.6189 -2.2326 0.1934 0.099464
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
Sensitivity analyses
GEE (No confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CUR6_BC_PAH6
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.4327 0.5261 -1.4639 0.5986 0.410887
## AgeAccelerationResidualHannum -0.2540 0.3933 -1.0249 0.5170 0.518488
## AgeAccelPheno 0.0501 0.4236 -0.7802 0.8804 0.905837
## DNAmAgeSkinBloodClockAdjAge -0.1426 0.3486 -0.8259 0.5408 0.682618
## AgeAccelGrim 0.5041 0.2530 0.0083 1.0000 0.046296
## DNAmTLAdjAge 0.0224 0.0182 -0.0133 0.0581 0.218580
## IEAA -0.2069 0.4547 -1.0980 0.6843 0.649101
## EEAA -0.5031 0.4863 -1.4563 0.4501 0.300898
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PAH31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2927 0.4855 -0.6590 1.2443 0.546688
## AgeAccelerationResidualHannum -0.0973 0.4055 -0.8922 0.6976 0.810379
## AgeAccelPheno 0.3208 0.4449 -0.5512 1.1929 0.470842
## DNAmAgeSkinBloodClockAdjAge 0.2373 0.3289 -0.4073 0.8820 0.470566
## AgeAccelGrim 0.7310 0.2308 0.2787 1.1833 0.001536
## DNAmTLAdjAge -0.0075 0.0153 -0.0375 0.0225 0.624570
## IEAA 0.2082 0.4946 -0.7613 1.1777 0.673866
## EEAA -0.1796 0.4935 -1.1470 0.7877 0.715875
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0066 0.4979 -0.9825 0.9693 0.989385
## AgeAccelerationResidualHannum -0.1890 0.4324 -1.0365 0.6585 0.662023
## AgeAccelPheno -0.4921 0.4292 -1.3334 0.3492 0.251578
## DNAmAgeSkinBloodClockAdjAge -0.1038 0.3592 -0.8079 0.6002 0.772563
## AgeAccelGrim 0.3318 0.2792 -0.2155 0.8790 0.234728
## DNAmTLAdjAge -0.0314 0.0180 -0.0667 0.0039 0.081107
## IEAA -0.0786 0.3990 -0.8607 0.7035 0.843903
## EEAA -0.1863 0.5715 -1.3065 0.9339 0.744433
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2388 0.3996 -0.5444 1.0220 0.550089
## AgeAccelerationResidualHannum -0.4357 0.3631 -1.1475 0.2760 0.230167
## AgeAccelPheno -0.3057 0.5258 -1.3363 0.7249 0.560995
## DNAmAgeSkinBloodClockAdjAge -0.3978 0.4081 -1.1978 0.4021 0.329702
## AgeAccelGrim 0.5233 0.3279 -0.1193 1.1660 0.110452
## DNAmTLAdjAge 0.0016 0.0193 -0.0363 0.0395 0.934335
## IEAA 0.4188 0.3701 -0.3066 1.1442 0.257784
## EEAA -0.5884 0.4984 -1.5653 0.3884 0.237708
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4132 0.3854 -0.3422 1.1686 0.283712
## AgeAccelerationResidualHannum -0.1611 0.3573 -0.8614 0.5391 0.652001
## AgeAccelPheno 0.2738 0.4339 -0.5767 1.1243 0.528104
## DNAmAgeSkinBloodClockAdjAge 0.2302 0.2808 -0.3202 0.7806 0.412357
## AgeAccelGrim 0.0075 0.3055 -0.5912 0.6062 0.980407
## DNAmTLAdjAge 0.0108 0.0163 -0.0212 0.0429 0.507892
## IEAA 0.3795 0.3685 -0.3428 1.1018 0.303102
## EEAA -0.2789 0.4877 -1.2348 0.6771 0.567494
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2894 0.4206 -1.1138 0.5349 0.491345
## AgeAccelerationResidualHannum -0.2600 0.4366 -1.1157 0.5958 0.551533
## AgeAccelPheno -0.4617 0.5188 -1.4785 0.5552 0.373538
## DNAmAgeSkinBloodClockAdjAge -0.3216 0.3716 -1.0500 0.4068 0.386817
## AgeAccelGrim -0.4236 0.2546 -0.9226 0.0754 0.096161
## DNAmTLAdjAge -0.0050 0.0162 -0.0367 0.0266 0.755569
## IEAA -0.4153 0.4082 -1.2154 0.3847 0.308902
## EEAA -0.3845 0.5022 -1.3687 0.5997 0.443884
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (No confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * BC\_PAH6 + \beta_2 PAH31 + \beta_3 NkF
+ \beta_4 PM\_RET + \beta_5 NO2 + \beta_6 SO2 \\
& + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CUR6_BC_PAH6
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.4791 0.9318 -3.3054 0.3472 0.112434
## AgeAccelerationResidualHannum -0.8897 0.6392 -2.1425 0.3632 0.163973
## AgeAccelPheno -1.0496 0.6158 -2.2566 0.1573 0.088284
## DNAmAgeSkinBloodClockAdjAge -0.9656 0.6733 -2.2852 0.3540 0.151524
## AgeAccelGrim 0.2121 0.4611 -0.6916 1.1159 0.645465
## DNAmTLAdjAge 0.0320 0.0283 -0.0234 0.0874 0.257913
## IEAA -0.9592 0.6824 -2.2968 0.3784 0.159862
## EEAA -1.4672 0.8234 -3.0810 0.1466 0.074755
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PAH31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.1663 0.7744 -0.3516 2.6842 0.132074
## AgeAccelerationResidualHannum 0.6958 0.6616 -0.6010 1.9926 0.292952
## AgeAccelPheno 1.4048 0.6513 0.1282 2.6814 0.031014
## DNAmAgeSkinBloodClockAdjAge 1.2198 0.5917 0.0601 2.3795 0.039242
## AgeAccelGrim 0.4197 0.4329 -0.4288 1.2681 0.332300
## DNAmTLAdjAge -0.0232 0.0254 -0.0729 0.0265 0.360187
## IEAA 0.5867 0.7383 -0.8604 2.0338 0.426826
## EEAA 0.9995 0.8353 -0.6377 2.6368 0.231474
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.6539 0.7722 -2.1675 0.8596 0.397091
## AgeAccelerationResidualHannum -0.2998 0.6360 -1.5463 0.9467 0.637364
## AgeAccelPheno -0.7892 0.5346 -1.8371 0.2587 0.139906
## DNAmAgeSkinBloodClockAdjAge -0.2456 0.6122 -1.4455 0.9544 0.688344
## AgeAccelGrim 0.2115 0.4320 -0.6352 1.0581 0.624439
## DNAmTLAdjAge -0.0234 0.0215 -0.0656 0.0188 0.277101
## IEAA -0.5455 0.5237 -1.5719 0.4810 0.297613
## EEAA -0.4052 0.7909 -1.9554 1.1449 0.608373
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1651 0.6256 -1.3913 1.0612 0.791917
## AgeAccelerationResidualHannum -0.6660 0.5443 -1.7328 0.4009 0.221139
## AgeAccelPheno -0.9117 0.5787 -2.0458 0.2225 0.115144
## DNAmAgeSkinBloodClockAdjAge -1.1032 0.5662 -2.2130 0.0066 0.051381
## AgeAccelGrim 0.1487 0.4027 -0.6406 0.9379 0.711948
## DNAmTLAdjAge 0.0156 0.0243 -0.0320 0.0632 0.520135
## IEAA 0.2341 0.5952 -0.9324 1.4006 0.694027
## EEAA -0.8919 0.7095 -2.2825 0.4988 0.208748
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4025 0.4540 -0.4873 1.2923 0.375315
## AgeAccelerationResidualHannum 0.0330 0.4339 -0.8175 0.8834 0.939450
## AgeAccelPheno 0.5912 0.4876 -0.3645 1.5470 0.225348
## DNAmAgeSkinBloodClockAdjAge 0.5744 0.3681 -0.1470 1.2959 0.118617
## AgeAccelGrim 0.0382 0.3264 -0.6016 0.6780 0.906878
## DNAmTLAdjAge 0.0132 0.0181 -0.0223 0.0488 0.466019
## IEAA 0.3493 0.4194 -0.4728 1.1714 0.404960
## EEAA -0.0383 0.5540 -1.1240 1.0475 0.944890
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.8208 0.5706 -1.9392 0.2976 0.150298
## AgeAccelerationResidualHannum -0.6214 0.5169 -1.6345 0.3916 0.229249
## AgeAccelPheno -0.8650 0.4893 -1.8241 0.0941 0.077121
## DNAmAgeSkinBloodClockAdjAge -0.8489 0.4693 -1.7687 0.0709 0.070478
## AgeAccelGrim -0.2685 0.2579 -0.7741 0.2371 0.297911
## DNAmTLAdjAge 0.0076 0.0199 -0.0313 0.0465 0.702412
## IEAA -0.7436 0.4988 -1.7213 0.2341 0.136059
## EEAA -0.9622 0.6072 -2.1523 0.2280 0.113064
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
3.2. Clusters based on model-based exposure estimates accrued before
age 18 (clusCHLD5)
The file “LEX_clusCHLD5.csv” has information on estimated pollutant
exposures during early childhood. Estimates are available for 5
different prototypes (cluster variables) for a total of 161 subjects and
211 visits. The prototypes are labelled as:
CHLD5_X7 – a cluster of 7 air pollutants
CHLD5_X33 – a large cluster of 33 air pollutants
CHLD5_NkF – NkF only
CHLD5_NO2 – NO2 only
CHLD5_SO2 – SO2 only
Summary the exposure estimates:
|
Smokeles
(N=18) |
Smoky
(N=98) |
Wood_and_or_Plant
(N=13) |
Overall
(N=129) |
| CHLD5_X7 |
|
|
|
|
| Mean (SD) |
-0.429 (1.11) |
-0.136 (0.900) |
1.17 (0.743) |
-0.0382 (1.00) |
| Median (IQR) |
-0.618 (0.819) |
0.108 (0.876) |
0.953 (0.935) |
0.111 (1.10) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CHLD5_X33 |
|
|
|
|
| Mean (SD) |
-0.570 (0.795) |
0.248 (1.02) |
0.354 (0.698) |
0.155 (1.00) |
| Median (IQR) |
-0.751 (1.35) |
0.290 (1.55) |
-0.106 (1.09) |
0.00445 (1.67) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CHLD5_NkF |
|
|
|
|
| Mean (SD) |
-0.206 (1.39) |
-0.194 (0.919) |
0.751 (0.607) |
-0.0978 (1.00) |
| Median (IQR) |
0.0257 (0.990) |
-0.405 (1.18) |
0.658 (0.655) |
-0.220 (1.34) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CHLD5_NO2 |
|
|
|
|
| Mean (SD) |
0.150 (0.851) |
0.203 (1.08) |
-0.0795 (0.299) |
0.167 (1.00) |
| Median (IQR) |
0.0788 (1.25) |
0.337 (1.25) |
-0.00902 (0.275) |
0.196 (1.16) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CHLD5_SO2 |
|
|
|
|
| Mean (SD) |
0.507 (1.20) |
-0.0262 (0.931) |
-0.413 (1.07) |
0.00157 (1.00) |
| Median (IQR) |
0.489 (1.52) |
0.365 (1.49) |
0.168 (1.68) |
0.362 (1.55) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
Primary analysis
GEE (with confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CHLD5_X7
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3203 0.4749 -0.6106 1.2511 0.500097
## AgeAccelerationResidualHannum 0.2537 0.4357 -0.6003 1.1078 0.560347
## AgeAccelPheno 0.5679 0.4709 -0.3551 1.4908 0.227818
## DNAmAgeSkinBloodClockAdjAge 0.1206 0.4010 -0.6654 0.9066 0.763649
## AgeAccelGrim 0.7240 0.2336 0.2661 1.1819 0.001940
## DNAmTLAdjAge 0.0023 0.0172 -0.0315 0.0361 0.894795
## IEAA 0.2835 0.4406 -0.5801 1.1472 0.519913
## EEAA 0.4553 0.5512 -0.6251 1.5357 0.408773
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_X33
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1755 0.4367 -0.6803 1.0314 0.687697
## AgeAccelerationResidualHannum 0.4243 0.4524 -0.4625 1.3110 0.348361
## AgeAccelPheno 1.0075 0.4154 0.1932 1.8217 0.015303
## DNAmAgeSkinBloodClockAdjAge 0.6385 0.3431 -0.0340 1.3110 0.062754
## AgeAccelGrim 0.9223 0.2677 0.3975 1.4471 0.000572
## DNAmTLAdjAge 0.0017 0.0168 -0.0311 0.0346 0.917831
## IEAA -0.1740 0.4295 -1.0159 0.6678 0.685351
## EEAA 0.6753 0.5473 -0.3974 1.7480 0.217233
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1209 0.3453 -0.5559 0.7977 0.726262
## AgeAccelerationResidualHannum 0.1766 0.3106 -0.4323 0.7854 0.569773
## AgeAccelPheno -0.1117 0.3836 -0.8636 0.6401 0.770817
## DNAmAgeSkinBloodClockAdjAge -0.1700 0.3331 -0.8229 0.4829 0.609819
## AgeAccelGrim 0.3823 0.2725 -0.1518 0.9164 0.160625
## DNAmTLAdjAge -0.0159 0.0210 -0.0572 0.0253 0.449153
## IEAA -0.0043 0.3132 -0.6181 0.6095 0.989039
## EEAA 0.2472 0.4245 -0.5847 1.0792 0.560268
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0810 0.3694 -0.8051 0.6430 0.826378
## AgeAccelerationResidualHannum 0.1768 0.3942 -0.5958 0.9495 0.653730
## AgeAccelPheno 0.1107 0.4220 -0.7165 0.9379 0.793118
## DNAmAgeSkinBloodClockAdjAge 0.5119 0.2997 -0.0755 1.0992 0.087629
## AgeAccelGrim -0.2112 0.2747 -0.7496 0.3271 0.441873
## DNAmTLAdjAge 0.0038 0.0184 -0.0322 0.0398 0.837019
## IEAA -0.1169 0.3200 -0.7441 0.5102 0.714759
## EEAA 0.1811 0.5004 -0.7997 1.1619 0.717381
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0463 0.5339 -1.0003 1.0928 0.930922
## AgeAccelerationResidualHannum 0.2517 0.4793 -0.6878 1.1912 0.599523
## AgeAccelPheno -0.0019 0.5192 -1.0195 1.0157 0.997117
## DNAmAgeSkinBloodClockAdjAge 0.3595 0.4092 -0.4426 1.1616 0.379712
## AgeAccelGrim -0.2383 0.2846 -0.7960 0.3195 0.402406
## DNAmTLAdjAge 0.0155 0.0197 -0.0231 0.0540 0.431373
## IEAA -0.0756 0.4624 -0.9819 0.8308 0.870176
## EEAA 0.1839 0.6127 -1.0170 1.3847 0.764120
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (with confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X7 + \beta_2 X33 + \beta_3 NkF +
\beta_4 NO2 + \beta_5 SO2 \\
& + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_9 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CHLD5_X7
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.5173 0.8594 -1.1671 2.2017 0.547229
## AgeAccelerationResidualHannum 0.1461 0.7143 -1.2540 1.5462 0.837978
## AgeAccelPheno -0.0658 0.7977 -1.6293 1.4978 0.934298
## DNAmAgeSkinBloodClockAdjAge -0.1961 0.7099 -1.5874 1.1952 0.782372
## AgeAccelGrim -0.0755 0.4012 -0.8619 0.7110 0.850819
## DNAmTLAdjAge 0.0288 0.0304 -0.0308 0.0884 0.343555
## IEAA 0.9209 0.7300 -0.5099 2.3518 0.207131
## EEAA 0.1427 0.9504 -1.7200 2.0054 0.880652
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_X33
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1383 0.7679 -1.6434 1.3668 0.857088
## AgeAccelerationResidualHannum 0.3052 0.5983 -0.8675 1.4778 0.610030
## AgeAccelPheno 1.0650 0.6767 -0.2613 2.3913 0.115534
## DNAmAgeSkinBloodClockAdjAge 0.7048 0.6063 -0.4835 1.8931 0.245050
## AgeAccelGrim 1.0035 0.3628 0.2923 1.7146 0.005679
## DNAmTLAdjAge -0.0165 0.0267 -0.0689 0.0359 0.537801
## IEAA -0.7294 0.6470 -1.9974 0.5387 0.259581
## EEAA 0.5619 0.7703 -0.9478 2.0716 0.465668
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0070 0.3689 -0.7300 0.7160 0.984906
## AgeAccelerationResidualHannum 0.1814 0.3400 -0.4850 0.8478 0.593662
## AgeAccelPheno -0.1419 0.3954 -0.9170 0.6331 0.719688
## DNAmAgeSkinBloodClockAdjAge -0.0496 0.3164 -0.6697 0.5706 0.875511
## AgeAccelGrim 0.3133 0.2781 -0.2317 0.8583 0.259852
## DNAmTLAdjAge -0.0213 0.0230 -0.0664 0.0238 0.354253
## IEAA -0.2292 0.3330 -0.8818 0.4235 0.491264
## EEAA 0.2362 0.4709 -0.6867 1.1591 0.615987
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1243 0.4357 -0.9783 0.7296 0.775344
## AgeAccelerationResidualHannum 0.0511 0.3906 -0.7145 0.8166 0.895992
## AgeAccelPheno -0.0185 0.4692 -0.9381 0.9011 0.968611
## DNAmAgeSkinBloodClockAdjAge 0.3481 0.3192 -0.2776 0.9738 0.275579
## AgeAccelGrim -0.2208 0.2747 -0.7593 0.3177 0.421498
## DNAmTLAdjAge -0.0057 0.0215 -0.0478 0.0365 0.792562
## IEAA -0.0512 0.4064 -0.8477 0.7452 0.899681
## EEAA 0.0693 0.5025 -0.9156 1.0542 0.890316
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3296 0.6923 -1.0273 1.6865 0.634011
## AgeAccelerationResidualHannum 0.2969 0.6400 -0.9574 1.5512 0.642721
## AgeAccelPheno -0.1400 0.5737 -1.2645 0.9845 0.807206
## DNAmAgeSkinBloodClockAdjAge 0.0461 0.4634 -0.8621 0.9544 0.920708
## AgeAccelGrim -0.1985 0.3401 -0.8652 0.4682 0.559458
## DNAmTLAdjAge 0.0278 0.0270 -0.0251 0.0808 0.302463
## IEAA 0.3504 0.6320 -0.8883 1.5891 0.579286
## EEAA 0.2054 0.8043 -1.3710 1.7818 0.798406
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
Sensitivity analyses
GEE (No confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CHLD5_X7
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2207 0.4948 -0.7491 1.1905 0.655601
## AgeAccelerationResidualHannum 0.2306 0.4283 -0.6089 1.0702 0.590299
## AgeAccelPheno 0.5869 0.4727 -0.3397 1.5135 0.214406
## DNAmAgeSkinBloodClockAdjAge 0.0972 0.4122 -0.7107 0.9051 0.813559
## AgeAccelGrim 0.7214 0.2483 0.2347 1.2082 0.003674
## DNAmTLAdjAge 0.0028 0.0170 -0.0306 0.0361 0.869658
## IEAA 0.2457 0.4662 -0.6681 1.1595 0.598215
## EEAA 0.3844 0.5429 -0.6798 1.4485 0.478971
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_X33
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0508 0.4358 -0.8034 0.9050 0.907218
## AgeAccelerationResidualHannum 0.3479 0.4268 -0.4887 1.1845 0.415049
## AgeAccelPheno 0.9349 0.3817 0.1869 1.6830 0.014303
## DNAmAgeSkinBloodClockAdjAge 0.5363 0.3296 -0.1098 1.1823 0.103750
## AgeAccelGrim 0.8280 0.2845 0.2703 1.3856 0.003612
## DNAmTLAdjAge 0.0051 0.0157 -0.0257 0.0358 0.746585
## IEAA -0.1733 0.4348 -1.0256 0.6789 0.690148
## EEAA 0.5134 0.5177 -0.5013 1.5280 0.321346
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1075 0.3600 -0.5981 0.8132 0.765179
## AgeAccelerationResidualHannum 0.1868 0.3273 -0.4546 0.8283 0.568062
## AgeAccelPheno -0.0105 0.3915 -0.7779 0.7569 0.978645
## DNAmAgeSkinBloodClockAdjAge -0.1434 0.3074 -0.7459 0.4590 0.640733
## AgeAccelGrim 0.4575 0.2894 -0.1097 1.0247 0.113931
## DNAmTLAdjAge -0.0206 0.0187 -0.0572 0.0160 0.269936
## IEAA -0.0038 0.3188 -0.6287 0.6210 0.990400
## EEAA 0.2502 0.4399 -0.6119 1.1124 0.569455
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2917 0.3327 -0.9438 0.3604 0.380559
## AgeAccelerationResidualHannum 0.0864 0.3415 -0.5829 0.7558 0.800235
## AgeAccelPheno 0.2815 0.3775 -0.4583 1.0213 0.455856
## DNAmAgeSkinBloodClockAdjAge 0.4451 0.2610 -0.0665 0.9568 0.088145
## AgeAccelGrim -0.1324 0.2318 -0.5867 0.3219 0.567850
## DNAmTLAdjAge -0.0006 0.0168 -0.0336 0.0324 0.970549
## IEAA -0.1348 0.2865 -0.6964 0.4269 0.638131
## EEAA -0.0333 0.4484 -0.9122 0.8456 0.940787
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2433 0.4985 -1.2203 0.7337 0.625524
## AgeAccelerationResidualHannum 0.2447 0.3982 -0.5358 1.0251 0.538895
## AgeAccelPheno 0.1729 0.4707 -0.7498 1.0955 0.713457
## DNAmAgeSkinBloodClockAdjAge 0.3229 0.3462 -0.3556 1.0014 0.350929
## AgeAccelGrim -0.2606 0.2583 -0.7669 0.2457 0.313027
## DNAmTLAdjAge 0.0095 0.0175 -0.0248 0.0437 0.587557
## IEAA -0.1967 0.4469 -1.0727 0.6793 0.659822
## EEAA 0.1014 0.5084 -0.8950 1.0979 0.841855
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (No confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X7 + \beta_2 X33 + \beta_3 NkF +
\beta_4 NO2 + \beta_5 SO2 \\
& + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CHLD5_X7
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1484 0.8386 -1.4952 1.7921 0.859505
## AgeAccelerationResidualHannum 0.1153 0.6560 -1.1704 1.4010 0.860459
## AgeAccelPheno 0.1116 0.7451 -1.3489 1.5721 0.880943
## DNAmAgeSkinBloodClockAdjAge -0.2280 0.6607 -1.5229 1.0670 0.730043
## AgeAccelGrim -0.1187 0.3727 -0.8491 0.6117 0.750076
## DNAmTLAdjAge 0.0224 0.0296 -0.0356 0.0804 0.449067
## IEAA 0.7039 0.7159 -0.6993 2.1072 0.325516
## EEAA 0.0332 0.8788 -1.6893 1.7557 0.969853
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_X33
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0098 0.7550 -1.4700 1.4897 0.989603
## AgeAccelerationResidualHannum 0.2667 0.5884 -0.8864 1.4199 0.650287
## AgeAccelPheno 0.8411 0.6240 -0.3820 2.0641 0.177714
## DNAmAgeSkinBloodClockAdjAge 0.6179 0.5494 -0.4590 1.6949 0.260729
## AgeAccelGrim 0.9523 0.3722 0.2229 1.6817 0.010498
## DNAmTLAdjAge -0.0095 0.0260 -0.0605 0.0415 0.715262
## IEAA -0.5905 0.6556 -1.8755 0.6945 0.367765
## EEAA 0.5105 0.7437 -0.9472 1.9682 0.492437
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0336 0.3782 -0.7077 0.7749 0.929218
## AgeAccelerationResidualHannum 0.1966 0.3443 -0.4783 0.8714 0.568124
## AgeAccelPheno 0.0042 0.4089 -0.7973 0.8057 0.991820
## DNAmAgeSkinBloodClockAdjAge -0.0166 0.3039 -0.6123 0.5790 0.956377
## AgeAccelGrim 0.4725 0.2908 -0.0975 1.0425 0.104207
## DNAmTLAdjAge -0.0259 0.0205 -0.0660 0.0142 0.206192
## IEAA -0.2108 0.3342 -0.8658 0.4443 0.528272
## EEAA 0.2653 0.4821 -0.6796 1.2102 0.582105
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2387 0.4147 -1.0515 0.5740 0.564799
## AgeAccelerationResidualHannum -0.0896 0.4099 -0.8930 0.7139 0.827055
## AgeAccelPheno 0.1399 0.4416 -0.7257 1.0055 0.751388
## DNAmAgeSkinBloodClockAdjAge 0.3202 0.3236 -0.3140 0.9544 0.322395
## AgeAccelGrim -0.0902 0.2507 -0.5815 0.4011 0.719025
## DNAmTLAdjAge -0.0094 0.0210 -0.0505 0.0318 0.655215
## IEAA -0.0342 0.3771 -0.7733 0.7050 0.927841
## EEAA -0.1734 0.5294 -1.2111 0.8643 0.743315
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0633 0.6438 -1.3252 1.1986 0.921700
## AgeAccelerationResidualHannum 0.3271 0.5647 -0.7798 1.4339 0.562446
## AgeAccelPheno 0.0473 0.5223 -0.9765 1.0711 0.927840
## DNAmAgeSkinBloodClockAdjAge 0.0072 0.3988 -0.7745 0.7888 0.985691
## AgeAccelGrim -0.2967 0.2952 -0.8753 0.2819 0.314878
## DNAmTLAdjAge 0.0198 0.0250 -0.0293 0.0689 0.428980
## IEAA 0.0923 0.5807 -1.0459 1.2306 0.873672
## EEAA 0.1829 0.6960 -1.1812 1.5471 0.792676
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
3.3. Clusters based on model-based lifetime exposure estimates
(clusCUM6)
The file “LEX_clus CUM6.csv” has information on estimated cumulative
pollutant exposures during the lifecourse. Estimates are available for 6
different prototypes (cluster variables) for a total of 161 subjects and
211 visits. The prototypes are labelled as:
CUM6_BC_NO2_PM – a cluster of BC, NO2, and PM
CUM6_PAH36 – a large cluster of 36 PAHs
CUM6_DlP – DlP only
CUM6_NkF – NkF only
CUM6_RET – retene only
CUM6_SO2 – SO2 only
Summary the exposure estimates:
|
Smokeles
(N=18) |
Smoky
(N=98) |
Wood_and_or_Plant
(N=13) |
Overall
(N=129) |
| CUM6_BC_NO2_PM |
|
|
|
|
| Mean (SD) |
0.0286 (0.741) |
0.0116 (0.992) |
0.987 (0.964) |
0.114 (1.00) |
| Median (IQR) |
0.185 (1.02) |
0.144 (1.80) |
1.20 (1.30) |
0.281 (1.44) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUM6_PAH36 |
|
|
|
|
| Mean (SD) |
-0.666 (0.807) |
0.284 (0.992) |
0.633 (0.688) |
0.199 (1.00) |
| Median (IQR) |
-0.820 (0.781) |
0.381 (1.77) |
0.692 (1.00) |
0.254 (1.77) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUM6_DlP |
|
|
|
|
| Mean (SD) |
0.603 (0.679) |
-0.356 (1.02) |
0.220 (0.578) |
-0.175 (1.00) |
| Median (IQR) |
0.627 (0.577) |
-0.607 (1.58) |
0.360 (0.860) |
-0.459 (1.71) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUM6_NkF |
|
|
|
|
| Mean (SD) |
-0.0774 (1.03) |
-0.112 (0.983) |
0.756 (0.791) |
-0.0183 (1.00) |
| Median (IQR) |
-0.0605 (0.651) |
-0.230 (1.16) |
0.938 (1.27) |
-0.0813 (1.10) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUM6_RET |
|
|
|
|
| Mean (SD) |
-0.285 (0.746) |
-0.309 (0.868) |
1.44 (0.851) |
-0.125 (1.00) |
| Median (IQR) |
-0.367 (0.966) |
-0.272 (0.980) |
1.49 (1.07) |
-0.215 (1.11) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
| CUM6_SO2 |
|
|
|
|
| Mean (SD) |
0.934 (1.04) |
-0.196 (0.904) |
-0.498 (0.919) |
-0.0834 (1.00) |
| Median (IQR) |
1.13 (1.38) |
0.0923 (1.32) |
-0.436 (1.38) |
0.0951 (1.20) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
Primary analysis
GEE (with confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CUM6_BC_NO2_PM
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4845 0.5774 -0.6473 1.6163 0.401437
## AgeAccelerationResidualHannum 0.5215 0.5330 -0.5231 1.5661 0.327785
## AgeAccelPheno 0.3823 0.6023 -0.7983 1.5628 0.525630
## DNAmAgeSkinBloodClockAdjAge -0.0072 0.5889 -1.1615 1.1471 0.990243
## AgeAccelGrim 0.9188 0.3485 0.2358 1.6018 0.008374
## DNAmTLAdjAge -0.0243 0.0191 -0.0617 0.0131 0.202354
## IEAA 0.6367 0.5722 -0.4848 1.7581 0.265808
## EEAA 0.8330 0.6549 -0.4505 2.1165 0.203371
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_PAH36
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4217 0.4927 -0.5439 1.3874 0.392004
## AgeAccelerationResidualHannum 0.3687 0.4569 -0.5269 1.2643 0.419765
## AgeAccelPheno 0.8814 0.4670 -0.0339 1.7967 0.059106
## DNAmAgeSkinBloodClockAdjAge 0.5245 0.3831 -0.2264 1.2754 0.170969
## AgeAccelGrim 1.1186 0.2478 0.6329 1.6043 0.000006
## DNAmTLAdjAge -0.0124 0.0178 -0.0473 0.0225 0.485675
## IEAA 0.3180 0.4711 -0.6054 1.2413 0.499732
## EEAA 0.5594 0.5466 -0.5119 1.6306 0.306101
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.5282 0.5095 -0.4704 1.5269 0.299874
## AgeAccelerationResidualHannum 0.0333 0.4767 -0.9010 0.9676 0.944277
## AgeAccelPheno -0.0644 0.4463 -0.9392 0.8104 0.885211
## DNAmAgeSkinBloodClockAdjAge 0.1336 0.3863 -0.6235 0.8907 0.729511
## AgeAccelGrim -0.5133 0.2342 -0.9723 -0.0542 0.028409
## DNAmTLAdjAge -0.0247 0.0190 -0.0618 0.0125 0.193466
## IEAA 0.6030 0.4524 -0.2836 1.4897 0.182510
## EEAA 0.1902 0.5983 -0.9824 1.3629 0.750505
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2324 0.3828 -0.5179 0.9827 0.543793
## AgeAccelerationResidualHannum 0.1781 0.3978 -0.6015 0.9578 0.654275
## AgeAccelPheno -0.0834 0.4364 -0.9387 0.7719 0.848483
## DNAmAgeSkinBloodClockAdjAge -0.1126 0.3796 -0.8566 0.6313 0.766665
## AgeAccelGrim 0.6217 0.2424 0.1466 1.0968 0.010318
## DNAmTLAdjAge -0.0408 0.0187 -0.0775 -0.0042 0.029067
## IEAA 0.1319 0.3345 -0.5237 0.7875 0.693302
## EEAA 0.3079 0.5257 -0.7224 1.3382 0.558067
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2761 0.4565 -0.6186 1.1708 0.545335
## AgeAccelerationResidualHannum -0.0012 0.3660 -0.7185 0.7161 0.997346
## AgeAccelPheno -0.1528 0.4926 -1.1183 0.8128 0.756506
## DNAmAgeSkinBloodClockAdjAge -0.2581 0.4393 -1.1192 0.6029 0.556832
## AgeAccelGrim 0.4957 0.3336 -0.1582 1.1496 0.137313
## DNAmTLAdjAge -0.0055 0.0181 -0.0410 0.0299 0.759396
## IEAA 0.3980 0.4067 -0.3991 1.1952 0.327743
## EEAA 0.0436 0.4976 -0.9318 1.0189 0.930251
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1719 0.5381 -1.2266 0.8828 0.749435
## AgeAccelerationResidualHannum -0.0028 0.5357 -1.0527 1.0471 0.995802
## AgeAccelPheno -0.4354 0.5591 -1.5314 0.6605 0.436120
## DNAmAgeSkinBloodClockAdjAge 0.0680 0.4547 -0.8233 0.9593 0.881166
## AgeAccelGrim -0.4510 0.2981 -1.0352 0.1332 0.130248
## DNAmTLAdjAge 0.0218 0.0211 -0.0197 0.0632 0.302801
## IEAA -0.0639 0.4681 -0.9813 0.8535 0.891383
## EEAA -0.2428 0.6476 -1.5120 1.0265 0.707765
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (with confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * BC\_NO2\_PM + \beta_2 PAH36 + \beta_3
DlP + \beta_4 NkF + \beta_5 RET + \beta_6 SO2 \\
& + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_10 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CUM6_BC_NO2_PM
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2763 0.6604 -1.5707 1.0180 0.675644
## AgeAccelerationResidualHannum 0.6226 0.7098 -0.7687 2.0139 0.380426
## AgeAccelPheno -0.2870 0.8336 -1.9210 1.3470 0.730643
## DNAmAgeSkinBloodClockAdjAge -0.7725 0.6705 -2.0867 0.5417 0.249299
## AgeAccelGrim 0.7237 0.4892 -0.2352 1.6825 0.139062
## DNAmTLAdjAge -0.0329 0.0285 -0.0887 0.0229 0.248074
## IEAA -0.0905 0.7019 -1.4661 1.2851 0.897410
## EEAA 0.9227 0.8794 -0.8010 2.6464 0.294080
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_PAH36
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.9170 0.6572 -0.3712 2.2051 0.162955
## AgeAccelerationResidualHannum 0.1258 0.6590 -1.1658 1.4173 0.848649
## AgeAccelPheno 1.5582 0.7851 0.0193 3.0970 0.047193
## DNAmAgeSkinBloodClockAdjAge 1.4211 0.6275 0.1912 2.6509 0.023528
## AgeAccelGrim 0.5152 0.4534 -0.3734 1.4038 0.255820
## DNAmTLAdjAge 0.0023 0.0284 -0.0533 0.0579 0.935476
## IEAA 0.6910 0.6494 -0.5818 1.9638 0.287306
## EEAA 0.2802 0.8353 -1.3570 1.9175 0.737260
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.1998 0.6428 -0.0600 2.4597 0.061950
## AgeAccelerationResidualHannum -0.1121 0.6183 -1.3239 1.0997 0.856146
## AgeAccelPheno 0.7278 0.5985 -0.4453 1.9008 0.223983
## DNAmAgeSkinBloodClockAdjAge 0.8458 0.5366 -0.2060 1.8975 0.114987
## AgeAccelGrim -0.5975 0.3523 -1.2880 0.0931 0.089941
## DNAmTLAdjAge -0.0077 0.0264 -0.0595 0.0441 0.771994
## IEAA 1.2753 0.5634 0.1711 2.3795 0.023597
## EEAA 0.1194 0.8013 -1.4513 1.6900 0.881598
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA <= 0.05
## EEAA > 0.05
##
## $CUM6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.4368 0.4664 -1.3509 0.4773 0.348997
## AgeAccelerationResidualHannum 0.2586 0.4247 -0.5738 1.0910 0.542573
## AgeAccelPheno -0.3908 0.5250 -1.4197 0.6381 0.456601
## DNAmAgeSkinBloodClockAdjAge -0.4464 0.4581 -1.3443 0.4515 0.329829
## AgeAccelGrim 0.7327 0.3128 0.1197 1.3458 0.019147
## DNAmTLAdjAge -0.0466 0.0224 -0.0905 -0.0026 0.037940
## IEAA -0.6473 0.4079 -1.4468 0.1523 0.112580
## EEAA 0.3088 0.5982 -0.8636 1.4813 0.605628
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3764 0.6370 -0.8720 1.6249 0.554524
## AgeAccelerationResidualHannum -0.4266 0.5577 -1.5197 0.6664 0.444265
## AgeAccelPheno -0.4741 0.5717 -1.5947 0.6465 0.406946
## DNAmAgeSkinBloodClockAdjAge -0.1786 0.5274 -1.2123 0.8550 0.734837
## AgeAccelGrim -0.4647 0.4455 -1.3380 0.4085 0.296920
## DNAmTLAdjAge 0.0313 0.0227 -0.0131 0.0758 0.167369
## IEAA 0.6498 0.5300 -0.3889 1.6885 0.220131
## EEAA -0.6094 0.7220 -2.0246 0.8057 0.398628
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2552 0.5589 -1.3506 0.8401 0.647869
## AgeAccelerationResidualHannum -0.0902 0.5722 -1.2118 1.0313 0.874673
## AgeAccelPheno -0.5914 0.5548 -1.6788 0.4959 0.286388
## DNAmAgeSkinBloodClockAdjAge -0.0308 0.4679 -0.9480 0.8863 0.947464
## AgeAccelGrim -0.3786 0.2916 -0.9501 0.1930 0.194243
## DNAmTLAdjAge 0.0318 0.0220 -0.0113 0.0750 0.147681
## IEAA -0.1138 0.4734 -1.0416 0.8140 0.810039
## EEAA -0.4334 0.7041 -1.8135 0.9467 0.538190
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
Sensitivity analyses
GEE (No confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CUM6_BC_NO2_PM
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1907 0.4016 -0.9779 0.5965 0.634967
## AgeAccelerationResidualHannum 0.4041 0.3040 -0.1918 1.0001 0.183765
## AgeAccelPheno 0.5086 0.4207 -0.3160 1.3331 0.226698
## DNAmAgeSkinBloodClockAdjAge -0.0894 0.3525 -0.7804 0.6015 0.799747
## AgeAccelGrim 0.5511 0.2483 0.0645 1.0377 0.026422
## DNAmTLAdjAge -0.0230 0.0168 -0.0560 0.0100 0.171787
## IEAA 0.1517 0.3657 -0.5651 0.8684 0.678375
## EEAA 0.4660 0.4074 -0.3325 1.2646 0.252646
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_PAH36
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0035 0.4159 -0.8187 0.8116 0.993245
## AgeAccelerationResidualHannum 0.3516 0.3621 -0.3581 1.0613 0.331534
## AgeAccelPheno 0.8308 0.3805 0.0851 1.5766 0.028989
## DNAmAgeSkinBloodClockAdjAge 0.3116 0.3196 -0.3148 0.9380 0.329588
## AgeAccelGrim 0.8438 0.2598 0.3345 1.3530 0.001165
## DNAmTLAdjAge -0.0121 0.0156 -0.0427 0.0185 0.437642
## IEAA 0.0972 0.3968 -0.6805 0.8749 0.806482
## EEAA 0.4134 0.4405 -0.4499 1.2768 0.347959
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2253 0.4472 -0.6513 1.1018 0.614480
## AgeAccelerationResidualHannum 0.0251 0.3973 -0.7537 0.8039 0.949610
## AgeAccelPheno 0.2101 0.3782 -0.5312 0.9513 0.578591
## DNAmAgeSkinBloodClockAdjAge 0.1132 0.3077 -0.4900 0.7163 0.713085
## AgeAccelGrim -0.1980 0.2248 -0.6385 0.2426 0.378458
## DNAmTLAdjAge -0.0286 0.0143 -0.0566 -0.0006 0.045023
## IEAA 0.4232 0.4009 -0.3626 1.2090 0.291198
## EEAA 0.0582 0.4987 -0.9193 1.0358 0.907068
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0462 0.3822 -0.7030 0.7953 0.903851
## AgeAccelerationResidualHannum 0.2010 0.3875 -0.5584 0.9604 0.603935
## AgeAccelPheno 0.1108 0.4263 -0.7248 0.9464 0.794927
## DNAmAgeSkinBloodClockAdjAge -0.1396 0.3429 -0.8117 0.5325 0.683932
## AgeAccelGrim 0.6449 0.2423 0.1699 1.1199 0.007787
## DNAmTLAdjAge -0.0426 0.0160 -0.0740 -0.0112 0.007855
## IEAA 0.0781 0.3237 -0.5564 0.7125 0.809440
## EEAA 0.2596 0.5127 -0.7454 1.2645 0.612683
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge <= 0.01
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0896 0.4721 -0.8358 1.0149 0.849533
## AgeAccelerationResidualHannum 0.0185 0.3607 -0.6883 0.7254 0.958982
## AgeAccelPheno -0.0094 0.4776 -0.9456 0.9268 0.984263
## DNAmAgeSkinBloodClockAdjAge -0.2770 0.4238 -1.1078 0.5537 0.513396
## AgeAccelGrim 0.5289 0.3255 -0.1092 1.1669 0.104230
## DNAmTLAdjAge -0.0096 0.0178 -0.0446 0.0254 0.590901
## IEAA 0.3264 0.4087 -0.4746 1.1275 0.424456
## EEAA -0.0023 0.4882 -0.9591 0.9546 0.996300
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.5100 0.4566 -1.4049 0.3848 0.263931
## AgeAccelerationResidualHannum 0.0593 0.3925 -0.7100 0.8287 0.879830
## AgeAccelPheno -0.0709 0.4767 -1.0053 0.8634 0.881716
## DNAmAgeSkinBloodClockAdjAge 0.0538 0.3518 -0.6356 0.7433 0.878374
## AgeAccelGrim -0.3800 0.2413 -0.8530 0.0929 0.115296
## DNAmTLAdjAge 0.0091 0.0168 -0.0239 0.0421 0.589293
## IEAA -0.2354 0.4139 -1.0467 0.5759 0.569554
## EEAA -0.2245 0.4736 -1.1529 0.7038 0.635479
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (No confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * BC\_NO2\_PM + \beta_2 PAH36 + \beta_3
DlP + \beta_4 NkF + \beta_5 RET + \beta_6 SO2 \\
& + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## The estimated effects:
## $CUM6_BC_NO2_PM
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.7215 0.6028 -1.9030 0.4601 0.231383
## AgeAccelerationResidualHannum 0.6129 0.6031 -0.5692 1.7949 0.309524
## AgeAccelPheno -0.2512 0.8497 -1.9166 1.4141 0.767463
## DNAmAgeSkinBloodClockAdjAge -0.9062 0.6312 -2.1434 0.3309 0.151068
## AgeAccelGrim 0.3279 0.4759 -0.6049 1.2607 0.490816
## DNAmTLAdjAge -0.0303 0.0300 -0.0891 0.0284 0.311544
## IEAA -0.4656 0.5941 -1.6301 0.6989 0.433222
## EEAA 0.8286 0.8113 -0.7615 2.4187 0.307097
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_PAH36
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.7687 0.6613 -0.5276 2.0649 0.245121
## AgeAccelerationResidualHannum 0.0063 0.6643 -1.2957 1.3082 0.992460
## AgeAccelPheno 1.5783 0.7955 0.0191 3.1374 0.047248
## DNAmAgeSkinBloodClockAdjAge 1.3622 0.6329 0.1217 2.6028 0.031378
## AgeAccelGrim 0.5138 0.4800 -0.4271 1.4547 0.284486
## DNAmTLAdjAge 0.0042 0.0273 -0.0493 0.0577 0.876807
## IEAA 0.6519 0.6564 -0.6346 1.9384 0.320609
## EEAA 0.0595 0.8325 -1.5723 1.6912 0.943066
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.9962 0.6371 -0.2526 2.2449 0.117931
## AgeAccelerationResidualHannum -0.2216 0.5656 -1.3302 0.8870 0.695227
## AgeAccelPheno 0.9789 0.6137 -0.2240 2.1818 0.110701
## DNAmAgeSkinBloodClockAdjAge 0.8691 0.5240 -0.1580 1.8961 0.097213
## AgeAccelGrim -0.2411 0.3994 -1.0240 0.5418 0.546119
## DNAmTLAdjAge -0.0113 0.0237 -0.0577 0.0351 0.633343
## IEAA 1.1569 0.5697 0.0403 2.2734 0.042276
## EEAA -0.1119 0.7544 -1.5906 1.3668 0.882045
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA <= 0.05
## EEAA > 0.05
##
## $CUM6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.3955 0.4845 -1.3451 0.5541 0.414304
## AgeAccelerationResidualHannum 0.3160 0.4623 -0.5902 1.2222 0.494353
## AgeAccelPheno -0.4256 0.5318 -1.4679 0.6167 0.423527
## DNAmAgeSkinBloodClockAdjAge -0.4871 0.4692 -1.4067 0.4325 0.299183
## AgeAccelGrim 0.6505 0.3495 -0.0346 1.3355 0.062747
## DNAmTLAdjAge -0.0458 0.0223 -0.0895 -0.0021 0.039894
## IEAA -0.6673 0.4153 -1.4814 0.1468 0.108133
## EEAA 0.3984 0.6451 -0.8659 1.6627 0.536858
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3079 0.6635 -0.9926 1.6084 0.642654
## AgeAccelerationResidualHannum -0.4837 0.5652 -1.5915 0.6240 0.392063
## AgeAccelPheno -0.3608 0.5698 -1.4777 0.7561 0.526663
## DNAmAgeSkinBloodClockAdjAge -0.1366 0.5252 -1.1659 0.8927 0.794734
## AgeAccelGrim -0.2531 0.4495 -1.1341 0.6280 0.573481
## DNAmTLAdjAge 0.0291 0.0222 -0.0144 0.0725 0.190098
## IEAA 0.6263 0.5358 -0.4238 1.6763 0.242437
## EEAA -0.7140 0.7308 -2.1464 0.7185 0.328603
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.6311 0.5032 -1.6174 0.3552 0.209806
## AgeAccelerationResidualHannum -0.1427 0.4936 -1.1101 0.8248 0.772525
## AgeAccelPheno -0.4667 0.5339 -1.5132 0.5797 0.382013
## DNAmAgeSkinBloodClockAdjAge -0.0384 0.4331 -0.8872 0.8104 0.929403
## AgeAccelGrim -0.5403 0.2612 -1.0522 -0.0284 0.038588
## DNAmTLAdjAge 0.0309 0.0196 -0.0075 0.0694 0.115126
## IEAA -0.4206 0.4231 -1.2500 0.4088 0.320235
## EEAA -0.5734 0.5975 -1.7446 0.5978 0.337231
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
3.0. All plots together
Not mutually adjusted
Mutually adjusted
3.4. Clusters based on pollutant measurements (clusMEAS6)
The file “LEX_clusMEAS6.csv” has information on measured pollutant
exposures during each visit. Estimates are available for 6 different
prototypes (cluster variables) for a total of 54 subjects and 54 visits.
The prototypes are labelled as:
MEAS6_BC_ PM_RET – a cluster of BC, PM, and retene
MEAS6_X31 – a large cluster of 31 air pollutants
MEAS6_X5 – a smaller cluster of 5 air pollutants
MEAS6_DlP – DlP only
MEAS6_NkF – NkF only
MEAS6_ NO2_SO2 – NO2, and SO2
Summary the exposure estimates:
|
Smokeles
(N=18) |
Smoky
(N=98) |
Wood_and_or_Plant
(N=13) |
Overall
(N=129) |
| MEAS6_BC_PM_RET |
|
|
|
|
| Mean (SD) |
-0.981 (0.892) |
0.0200 (0.644) |
0.889 (1.24) |
-0.0486 (1.00) |
| Median (IQR) |
-0.801 (1.64) |
0.0540 (1.06) |
0.570 (2.17) |
-0.104 (1.08) |
| Missing |
4 (22.2%) |
58 (59.2%) |
2 (15.4%) |
64 (49.6%) |
| MEAS6_X31 |
|
|
|
|
| Mean (SD) |
-1.13 (0.591) |
0.302 (0.903) |
0.315 (0.721) |
-0.00352 (1.00) |
| Median (IQR) |
-0.901 (1.07) |
0.257 (0.745) |
0.309 (0.830) |
0.125 (1.27) |
| Missing |
4 (22.2%) |
58 (59.2%) |
2 (15.4%) |
64 (49.6%) |
| MEAS6_X5 |
|
|
|
|
| Mean (SD) |
-0.984 (0.222) |
0.292 (1.03) |
0.269 (0.671) |
0.0136 (1.00) |
| Median (IQR) |
-1.04 (0.0759) |
0.634 (1.93) |
0.0397 (0.983) |
-0.102 (2.00) |
| Missing |
4 (22.2%) |
58 (59.2%) |
2 (15.4%) |
64 (49.6%) |
| MEAS6_DlP |
|
|
|
|
| Mean (SD) |
0.386 (0.850) |
-0.0493 (1.07) |
-0.201 (0.877) |
0.0186 (1.00) |
| Median (IQR) |
0.534 (1.72) |
-0.693 (2.24) |
-0.509 (0.601) |
-0.627 (2.02) |
| Missing |
4 (22.2%) |
58 (59.2%) |
2 (15.4%) |
64 (49.6%) |
| MEAS6_NkF |
|
|
|
|
| Mean (SD) |
-0.0704 (0.936) |
-0.0557 (0.979) |
0.345 (1.17) |
0.00902 (1.00) |
| Median (IQR) |
-0.447 (0.261) |
-0.508 (0.686) |
0.160 (2.25) |
-0.489 (1.53) |
| Missing |
4 (22.2%) |
58 (59.2%) |
2 (15.4%) |
64 (49.6%) |
| MEAS6_NO2_SO2 |
|
|
|
|
| Mean (SD) |
1.03 (0.949) |
-0.159 (0.871) |
-0.203 (0.824) |
0.0902 (1.00) |
| Median (IQR) |
1.23 (1.07) |
-0.410 (1.58) |
-0.114 (1.20) |
-0.0368 (1.71) |
| Missing |
4 (22.2%) |
58 (59.2%) |
2 (15.4%) |
64 (49.6%) |
Primary analysis
GEE (with confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## The estimated effects:
## $MEAS6_BC_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2235 0.4649 -1.1346 0.6876 0.630706
## AgeAccelerationResidualHannum -0.3869 0.4214 -1.2128 0.4391 0.358599
## AgeAccelPheno 0.2398 0.7366 -1.2040 1.6836 0.744748
## DNAmAgeSkinBloodClockAdjAge -0.0281 0.4061 -0.8241 0.7679 0.944853
## AgeAccelGrim 0.7262 0.4968 -0.2477 1.7000 0.143868
## DNAmTLAdjAge -0.0015 0.0310 -0.0622 0.0592 0.960601
## IEAA -0.5086 0.4080 -1.3083 0.2911 0.212570
## EEAA -0.4819 0.5028 -1.4675 0.5037 0.337897
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.1701 0.6664 -0.1361 2.4763 0.079130
## AgeAccelerationResidualHannum 0.8578 0.6488 -0.4139 2.1295 0.186160
## AgeAccelPheno 1.5099 0.6844 0.1684 2.8514 0.027378
## DNAmAgeSkinBloodClockAdjAge 1.3861 0.5750 0.2591 2.5131 0.015923
## AgeAccelGrim 1.2466 0.3509 0.5588 1.9344 0.000382
## DNAmTLAdjAge -0.0299 0.0270 -0.0829 0.0231 0.269001
## IEAA 0.4612 0.5256 -0.5690 1.4914 0.380214
## EEAA 1.0981 0.7092 -0.2918 2.4881 0.121504
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X5
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1185 0.6917 -1.4742 1.2373 0.864003
## AgeAccelerationResidualHannum -0.7014 0.7604 -2.1918 0.7891 0.356353
## AgeAccelPheno 0.6101 0.7200 -0.8012 2.0214 0.396808
## DNAmAgeSkinBloodClockAdjAge 0.6642 0.5957 -0.5033 1.8317 0.264838
## AgeAccelGrim 1.0801 0.3956 0.3048 1.8554 0.006324
## DNAmTLAdjAge 0.0264 0.0295 -0.0314 0.0842 0.370662
## IEAA 0.1603 0.5546 -0.9268 1.2473 0.772629
## EEAA -1.1103 0.9127 -2.8993 0.6786 0.223780
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3300 0.7501 -1.1403 1.8003 0.659985
## AgeAccelerationResidualHannum 0.1401 0.6599 -1.1534 1.4335 0.831909
## AgeAccelPheno 0.3848 0.7064 -0.9998 1.7694 0.585940
## DNAmAgeSkinBloodClockAdjAge -0.8645 0.5653 -1.9724 0.2434 0.126183
## AgeAccelGrim -0.2800 0.5069 -1.2736 0.7135 0.580644
## DNAmTLAdjAge -0.0293 0.0330 -0.0939 0.0353 0.373836
## IEAA 0.3878 0.5744 -0.7380 1.5136 0.499591
## EEAA 0.1578 0.8349 -1.4786 1.7942 0.850081
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1121 0.6810 -1.2226 1.4467 0.869263
## AgeAccelerationResidualHannum 0.6400 0.5299 -0.3987 1.6786 0.227160
## AgeAccelPheno 0.2972 0.7165 -1.1072 1.7015 0.678309
## DNAmAgeSkinBloodClockAdjAge 0.2329 0.5130 -0.7727 1.2384 0.649921
## AgeAccelGrim -0.1077 0.3905 -0.8731 0.6576 0.782640
## DNAmTLAdjAge -0.0093 0.0264 -0.0610 0.0425 0.725886
## IEAA -0.2804 0.6046 -1.4655 0.9047 0.642784
## EEAA 0.9416 0.7168 -0.4633 2.3465 0.188958
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NO2_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1727 0.5901 -1.3293 0.9840 0.769835
## AgeAccelerationResidualHannum 0.6586 0.5626 -0.4441 1.7613 0.241740
## AgeAccelPheno -0.0504 0.5508 -1.1300 1.0292 0.927106
## DNAmAgeSkinBloodClockAdjAge -0.1215 0.4111 -0.9274 0.6843 0.767587
## AgeAccelGrim -0.3842 0.4393 -1.2452 0.4768 0.381806
## DNAmTLAdjAge -0.0237 0.0351 -0.0924 0.0450 0.498829
## IEAA -0.2847 0.4898 -1.2447 0.6753 0.561073
## EEAA 0.3306 0.6766 -0.9956 1.6567 0.625144
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (with confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * BC\_PAH6 + \beta_2 PAH31 + \beta_3 NkF
+ \beta_4 PM\_RET + \beta_5 NO2 + \beta_6 SO2 \\
& + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_10 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## The estimated effects:
## $MEAS6_BC_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.8442 0.8002 -2.4126 0.7241 0.291397
## AgeAccelerationResidualHannum -0.7346 0.6279 -1.9654 0.4962 0.242077
## AgeAccelPheno -0.6428 0.9775 -2.5588 1.2731 0.510787
## DNAmAgeSkinBloodClockAdjAge -1.0181 0.6484 -2.2889 0.2526 0.116334
## AgeAccelGrim 0.0970 0.6736 -1.2233 1.4173 0.885493
## DNAmTLAdjAge -0.0012 0.0336 -0.0671 0.0646 0.971244
## IEAA -1.0294 0.6960 -2.3936 0.3348 0.139154
## EEAA -0.9299 0.7213 -2.3437 0.4839 0.197339
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 2.9236 1.1604 0.6492 5.1980 0.011755
## AgeAccelerationResidualHannum 2.6504 0.7066 1.2654 4.0354 0.000176
## AgeAccelPheno 2.1706 1.0764 0.0609 4.2803 0.043736
## DNAmAgeSkinBloodClockAdjAge 2.6592 0.7639 1.1619 4.1565 0.000500
## AgeAccelGrim 1.3227 0.6515 0.0457 2.5997 0.042342
## DNAmTLAdjAge -0.0948 0.0435 -0.1801 -0.0095 0.029327
## IEAA 1.3100 0.9983 -0.6466 3.2666 0.189427
## EEAA 3.6450 0.9443 1.7943 5.4958 0.000113
## sig_level
## AgeAccelerationResidual <= 0.05
## AgeAccelerationResidualHannum <= 0.001
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.001
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA <= 0.001
##
## $MEAS6_X5
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.7629 1.2190 -4.1520 0.6263 0.148124
## AgeAccelerationResidualHannum -2.0056 1.1445 -4.2488 0.2375 0.079695
## AgeAccelPheno -0.4738 1.0914 -2.6129 1.6654 0.664231
## DNAmAgeSkinBloodClockAdjAge -0.8129 0.8632 -2.5047 0.8790 0.346343
## AgeAccelGrim -0.0183 0.8222 -1.6299 1.5933 0.982244
## DNAmTLAdjAge 0.0893 0.0468 -0.0024 0.1811 0.056355
## IEAA -0.3050 1.0658 -2.3940 1.7840 0.774720
## EEAA -3.0415 1.3883 -5.7626 -0.3205 0.028463
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA <= 0.05
##
## $MEAS6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2054 0.7038 -1.5847 1.1740 0.770413
## AgeAccelerationResidualHannum -0.4336 0.5711 -1.5529 0.6857 0.447663
## AgeAccelPheno 0.1028 0.6233 -1.1188 1.3245 0.868985
## DNAmAgeSkinBloodClockAdjAge -1.2543 0.5095 -2.2530 -0.2557 0.013825
## AgeAccelGrim -0.3890 0.4136 -1.1996 0.4216 0.346879
## DNAmTLAdjAge -0.0106 0.0251 -0.0597 0.0386 0.674109
## IEAA 0.2089 0.6079 -0.9826 1.4004 0.731134
## EEAA -0.6465 0.6768 -1.9731 0.6800 0.339449
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.7458 0.7332 -2.1828 0.6913 0.309090
## AgeAccelerationResidualHannum -0.0529 0.6964 -1.4179 1.3120 0.939408
## AgeAccelPheno -0.1289 0.7565 -1.6116 1.3538 0.864719
## DNAmAgeSkinBloodClockAdjAge -0.3124 0.5499 -1.3901 0.7653 0.569944
## AgeAccelGrim -0.4633 0.5354 -1.5126 0.5859 0.386765
## DNAmTLAdjAge 0.0239 0.0316 -0.0379 0.0858 0.447937
## IEAA -0.5446 0.7470 -2.0087 0.9196 0.466021
## EEAA -0.2025 0.8570 -1.8823 1.4772 0.813191
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NO2_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2827 0.6034 -1.4653 0.9000 0.639443
## AgeAccelerationResidualHannum 0.6944 0.5222 -0.3291 1.7179 0.183612
## AgeAccelPheno 0.0407 0.5888 -1.1133 1.1948 0.944826
## DNAmAgeSkinBloodClockAdjAge -0.0515 0.4726 -0.9778 0.8747 0.913152
## AgeAccelGrim -0.2765 0.4560 -1.1704 0.6174 0.544315
## DNAmTLAdjAge -0.0348 0.0344 -0.1023 0.0327 0.312124
## IEAA -0.4865 0.4860 -1.4391 0.4661 0.316810
## EEAA 0.3428 0.6100 -0.8529 1.5385 0.574166
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
Sensitivity analyses
GEE (no confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## The estimated effects:
## $MEAS6_BC_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2273 0.4596 -1.1280 0.6735 0.620946
## AgeAccelerationResidualHannum -0.4691 0.4258 -1.3037 0.3654 0.270532
## AgeAccelPheno 0.1658 0.7025 -1.2110 1.5426 0.813385
## DNAmAgeSkinBloodClockAdjAge -0.1434 0.3744 -0.8772 0.5903 0.701662
## AgeAccelGrim 0.7259 0.4744 -0.2039 1.6556 0.125954
## DNAmTLAdjAge 0.0076 0.0308 -0.0527 0.0679 0.804700
## IEAA -0.4021 0.4108 -1.2072 0.4030 0.327633
## EEAA -0.6514 0.5359 -1.7017 0.3989 0.224115
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.1450 0.7420 -0.3094 2.5993 0.122821
## AgeAccelerationResidualHannum 0.7839 0.7130 -0.6137 2.1814 0.271617
## AgeAccelPheno 1.2670 0.6722 -0.0505 2.5845 0.059442
## DNAmAgeSkinBloodClockAdjAge 1.1984 0.6127 -0.0025 2.3992 0.050466
## AgeAccelGrim 1.1773 0.3690 0.4540 1.9005 0.001421
## DNAmTLAdjAge -0.0211 0.0284 -0.0768 0.0347 0.458882
## IEAA 0.5421 0.5570 -0.5496 1.6338 0.330427
## EEAA 0.9391 0.8445 -0.7161 2.5942 0.266125
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X5
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.3062 0.6179 -1.5172 0.9049 0.620223
## AgeAccelerationResidualHannum -0.8910 0.6023 -2.0715 0.2895 0.139057
## AgeAccelPheno 0.3543 0.5860 -0.7943 1.5028 0.545474
## DNAmAgeSkinBloodClockAdjAge 0.2646 0.5224 -0.7592 1.2885 0.612459
## AgeAccelGrim 0.7872 0.3828 0.0369 1.5375 0.039742
## DNAmTLAdjAge 0.0410 0.0252 -0.0085 0.0905 0.104142
## IEAA 0.2231 0.4725 -0.7029 1.1492 0.636736
## EEAA -1.4648 0.7266 -2.8889 -0.0406 0.043809
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA <= 0.05
##
## $MEAS6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4187 0.6516 -0.8583 1.6958 0.520439
## AgeAccelerationResidualHannum 0.2965 0.6087 -0.8966 1.4895 0.626220
## AgeAccelPheno 0.4575 0.6157 -0.7494 1.6643 0.457527
## DNAmAgeSkinBloodClockAdjAge -0.5603 0.5512 -1.6408 0.5201 0.309375
## AgeAccelGrim -0.1807 0.5261 -1.2118 0.8504 0.731227
## DNAmTLAdjAge -0.0383 0.0303 -0.0978 0.0212 0.206611
## IEAA 0.2692 0.4920 -0.6952 1.2335 0.584322
## EEAA 0.4528 0.7738 -1.0639 1.9694 0.558486
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2553 0.7317 -1.1788 1.6894 0.727152
## AgeAccelerationResidualHannum 0.7447 0.5576 -0.3482 1.8376 0.181691
## AgeAccelPheno 0.2877 0.6934 -1.0715 1.6469 0.678228
## DNAmAgeSkinBloodClockAdjAge 0.2499 0.6033 -0.9326 1.4324 0.678727
## AgeAccelGrim 0.0147 0.4188 -0.8061 0.8355 0.972011
## DNAmTLAdjAge -0.0130 0.0256 -0.0631 0.0372 0.612024
## IEAA -0.2371 0.6548 -1.5205 1.0462 0.717235
## EEAA 1.1009 0.7549 -0.3787 2.5805 0.144742
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NO2_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2227 0.6304 -1.0129 1.4582 0.723915
## AgeAccelerationResidualHannum 0.7680 0.5370 -0.2846 1.8206 0.152691
## AgeAccelPheno 0.1081 0.5723 -1.0137 1.2298 0.850220
## DNAmAgeSkinBloodClockAdjAge 0.3792 0.5139 -0.6281 1.3866 0.460562
## AgeAccelGrim -0.0593 0.3930 -0.8295 0.7109 0.880064
## DNAmTLAdjAge -0.0287 0.0278 -0.0832 0.0258 0.302204
## IEAA 0.1173 0.5713 -1.0024 1.2370 0.837289
## EEAA 0.5627 0.6616 -0.7340 1.8593 0.395047
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (no confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * BC\_PAH6 + \beta_2 PAH31 + \beta_3 NkF
+ \beta_4 PM\_RET + \beta_5 NO2 + \beta_6 SO2 \\
+ \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## [1] "Fitting with 65 observations."
## The estimated effects:
## $MEAS6_BC_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.7766 0.8129 -2.3699 0.8167 0.339420
## AgeAccelerationResidualHannum -0.7162 0.6484 -1.9870 0.5545 0.269288
## AgeAccelPheno -0.6326 0.9995 -2.5916 1.3264 0.526781
## DNAmAgeSkinBloodClockAdjAge -0.9386 0.6237 -2.1611 0.2839 0.132386
## AgeAccelGrim 0.1857 0.6923 -1.1712 1.5425 0.788542
## DNAmTLAdjAge -0.0019 0.0364 -0.0733 0.0696 0.959376
## IEAA -0.9607 0.6998 -2.3323 0.4109 0.169814
## EEAA -0.8957 0.7573 -2.3799 0.5885 0.236893
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 2.9212 1.1725 0.6231 5.2194 0.012724
## AgeAccelerationResidualHannum 2.5414 0.7325 1.1057 3.9771 0.000521
## AgeAccelPheno 1.9102 1.0881 -0.2225 4.0429 0.079169
## DNAmAgeSkinBloodClockAdjAge 2.5272 0.7861 0.9865 4.0680 0.001305
## AgeAccelGrim 1.3362 0.6026 0.1551 2.5173 0.026600
## DNAmTLAdjAge -0.0893 0.0381 -0.1640 -0.0146 0.019192
## IEAA 1.3108 1.0598 -0.7664 3.3880 0.216150
## EEAA 3.5053 0.9356 1.6715 5.3391 0.000179
## sig_level
## AgeAccelerationResidual <= 0.05
## AgeAccelerationResidualHannum <= 0.001
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.01
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA <= 0.001
##
## $MEAS6_X5
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.7586 1.1657 -4.0433 0.5261 0.131376
## AgeAccelerationResidualHannum -1.9560 0.9586 -3.8348 -0.0772 0.041296
## AgeAccelPheno -0.3865 0.9733 -2.2941 1.5211 0.691284
## DNAmAgeSkinBloodClockAdjAge -1.0161 0.8551 -2.6920 0.6599 0.234721
## AgeAccelGrim -0.2227 0.8173 -1.8247 1.3792 0.785228
## DNAmTLAdjAge 0.0914 0.0396 0.0137 0.1690 0.021045
## IEAA -0.1314 1.0418 -2.1733 1.9105 0.899613
## EEAA -3.0913 1.1474 -5.3402 -0.8424 0.007057
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum <= 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA <= 0.01
##
## $MEAS6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1335 0.6921 -1.4900 1.2230 0.847044
## AgeAccelerationResidualHannum -0.3653 0.5500 -1.4434 0.7128 0.506618
## AgeAccelPheno 0.3072 0.5678 -0.8057 1.4202 0.588463
## DNAmAgeSkinBloodClockAdjAge -0.9589 0.5153 -1.9689 0.0511 0.062771
## AgeAccelGrim -0.2213 0.4372 -1.0783 0.6357 0.612772
## DNAmTLAdjAge -0.0129 0.0232 -0.0584 0.0326 0.579565
## IEAA 0.1337 0.5872 -1.0172 1.2846 0.819896
## EEAA -0.4873 0.6530 -1.7673 0.7926 0.455513
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.6608 0.7805 -2.1905 0.8690 0.397222
## AgeAccelerationResidualHannum -0.0377 0.6879 -1.3860 1.3106 0.956263
## AgeAccelPheno -0.1113 0.7884 -1.6566 1.4340 0.887744
## DNAmAgeSkinBloodClockAdjAge -0.3369 0.6614 -1.6332 0.9594 0.610519
## AgeAccelGrim -0.3873 0.5449 -1.4553 0.6806 0.477185
## DNAmTLAdjAge 0.0236 0.0303 -0.0359 0.0830 0.437352
## IEAA -0.4197 0.7938 -1.9754 1.1361 0.597021
## EEAA -0.1953 0.8460 -1.8533 1.4628 0.817468
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NO2_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0388 0.6932 -1.3198 1.3974 0.955344
## AgeAccelerationResidualHannum 0.6650 0.6019 -0.5148 1.8448 0.269254
## AgeAccelPheno 0.1054 0.5999 -1.0704 1.2813 0.860498
## DNAmAgeSkinBloodClockAdjAge 0.4092 0.5403 -0.6498 1.4682 0.448831
## AgeAccelGrim 0.0773 0.4291 -0.7637 0.9183 0.857074
## DNAmTLAdjAge -0.0212 0.0274 -0.0748 0.0325 0.438814
## IEAA -0.0223 0.6156 -1.2288 1.1842 0.971078
## EEAA 0.3465 0.7098 -1.0448 1.7377 0.625475
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
3.5. Clusters based on urinary biomarkers (clusURI5)
The file “LEX_clusURI5.csv” has information on measured urinary
biomarkers obtained during each visit. Estimates are available for 5
different prototypes (cluster variables) for a total of 163 subjects and
186 visits. The prototypes are labelled as:
URI5_NAP_1M_2M – a cluster of Naphthalene, 1Methylnaphthalene, and
2Methylnaphthalene
URI5_ACE – Acenaphthene only
URI5_FLU_PHE – Fluoranthene and Phenanthrene_anth
URI5_PYR – Pyrene only
URI5_CHR – Baa_Chrysene only
Summary the exposure estimates:
|
Smokeles
(N=18) |
Smoky
(N=98) |
Wood_and_or_Plant
(N=13) |
Overall
(N=129) |
| URI5_NAP_1M_2M |
|
|
|
|
| Mean (SD) |
-0.299 (1.27) |
0.0278 (0.941) |
0.539 (0.948) |
0.0150 (1.00) |
| Median (IQR) |
-0.707 (0.631) |
0.188 (1.06) |
0.468 (0.984) |
0.0747 (1.26) |
| Missing |
3 (16.7%) |
16 (16.3%) |
6 (46.2%) |
25 (19.4%) |
| URI5_ACE |
|
|
|
|
| Mean (SD) |
-0.188 (0.674) |
-0.0411 (1.04) |
0.0303 (1.19) |
-0.0574 (1.00) |
| Median (IQR) |
-0.271 (0.719) |
-0.0410 (1.53) |
-0.438 (1.47) |
-0.0912 (1.31) |
| Missing |
3 (16.7%) |
16 (16.3%) |
6 (46.2%) |
25 (19.4%) |
| URI5_FLU_PHE |
|
|
|
|
| Mean (SD) |
-0.135 (1.07) |
-0.0897 (0.980) |
0.622 (0.980) |
-0.0484 (1.00) |
| Median (IQR) |
-0.171 (1.34) |
0.0539 (1.29) |
0.564 (0.908) |
0.0355 (1.32) |
| Missing |
3 (16.7%) |
16 (16.3%) |
6 (46.2%) |
25 (19.4%) |
| URI5_PYR |
|
|
|
|
| Mean (SD) |
-0.322 (1.04) |
0.0209 (0.980) |
0.145 (1.18) |
-0.0201 (1.00) |
| Median (IQR) |
-0.212 (0.946) |
0.138 (0.814) |
0.518 (0.718) |
0.0727 (0.825) |
| Missing |
3 (16.7%) |
16 (16.3%) |
6 (46.2%) |
25 (19.4%) |
| URI5_CHR |
|
|
|
|
| Mean (SD) |
0.00119 (0.728) |
-0.0218 (1.07) |
0.0865 (0.710) |
-0.0112 (1.00) |
| Median (IQR) |
-0.176 (0.957) |
0.106 (0.962) |
-0.203 (0.518) |
-0.0392 (0.957) |
| Missing |
3 (16.7%) |
16 (16.3%) |
6 (46.2%) |
25 (19.4%) |
Primary analysis
GEE (with confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## The estimated effects:
## $URI5_NAP_1M_2M
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1161 0.4703 -0.8056 1.0378 0.805060
## AgeAccelerationResidualHannum -0.5106 0.4543 -1.4010 0.3798 0.261037
## AgeAccelPheno -0.0736 0.4332 -0.9226 0.7755 0.865183
## DNAmAgeSkinBloodClockAdjAge -0.2509 0.3697 -0.9754 0.4736 0.497306
## AgeAccelGrim 0.4434 11.1347 -21.3806 22.2675 0.968232
## DNAmTLAdjAge -0.0183 0.0263 -0.0698 0.0333 0.487524
## IEAA -0.0681 0.5093 -1.0664 0.9301 0.893583
## EEAA -0.0646 0.5047 -1.0539 0.9247 0.898139
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_ACE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0712 0.4126 -0.8798 0.7374 0.862967
## AgeAccelerationResidualHannum 0.4653 0.3227 -0.1673 1.0978 0.149403
## AgeAccelPheno 0.8476 0.4642 -0.0622 1.7574 0.067864
## DNAmAgeSkinBloodClockAdjAge -0.0601 0.3118 -0.6711 0.5510 0.847258
## AgeAccelGrim -1.0812 0.3313 -1.7305 -0.4319 0.001099
## DNAmTLAdjAge -0.0036 0.0163 -0.0355 0.0283 0.825396
## IEAA 0.2433 0.4079 -0.5562 1.0427 0.550878
## EEAA 0.5842 0.4383 -0.2750 1.4433 0.182651
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_FLU_PHE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2144 0.4248 -0.6183 1.0471 0.613878
## AgeAccelerationResidualHannum 0.2193 0.4268 -0.6173 1.0559 0.607443
## AgeAccelPheno 0.2969 0.3867 -0.4609 1.0548 0.442547
## DNAmAgeSkinBloodClockAdjAge 0.0411 0.3112 -0.5689 0.6510 0.895024
## AgeAccelGrim 0.7403 0.6902 -0.6125 2.0931 0.283461
## DNAmTLAdjAge -0.0280 0.0399 -0.1063 0.0503 0.483121
## IEAA 0.1384 0.4352 -0.7146 0.9915 0.750418
## EEAA 0.5106 0.5566 -0.5803 1.6015 0.358916
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_PYR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0156 0.4182 -0.8353 0.8042 0.970335
## AgeAccelerationResidualHannum 0.3796 0.3834 -0.3719 1.1311 0.322117
## AgeAccelPheno 0.9330 0.4415 0.0677 1.7982 0.034566
## DNAmAgeSkinBloodClockAdjAge 0.6225 0.3784 -0.1193 1.3642 0.099996
## AgeAccelGrim 0.5647 0.3108 -0.0445 1.1739 0.069265
## DNAmTLAdjAge -0.0308 0.0241 -0.0781 0.0165 0.201847
## IEAA -0.0646 0.4286 -0.9046 0.7754 0.880226
## EEAA 0.5890 0.4918 -0.3749 1.5529 0.231038
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_CHR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0831 0.3668 -0.6359 0.8020 0.820849
## AgeAccelerationResidualHannum 0.1034 0.4326 -0.7445 0.9513 0.811097
## AgeAccelPheno -0.0590 0.3733 -0.7908 0.6727 0.874324
## DNAmAgeSkinBloodClockAdjAge 0.0941 0.3167 -0.5266 0.7148 0.766339
## AgeAccelGrim 0.3365 0.3409 -0.3316 1.0047 0.323563
## DNAmTLAdjAge -0.0196 0.2888 -0.5856 0.5464 0.945801
## IEAA -0.2298 0.4192 -1.0514 0.5919 0.583623
## EEAA 0.3196 0.5114 -0.6828 1.3220 0.532007
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (with confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * NAP\_1M\_2M + \beta_2 ACE + \beta_3
FLU\_PHE + \beta_4 PYR + \beta_5 CHR \\
& + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_9 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## The estimated effects:
## $URI5_NAP_1M_2M
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0420 0.5761 -1.0871 1.1711 0.941836
## AgeAccelerationResidualHannum -1.1902 0.5009 -2.1719 -0.2086 0.017483
## AgeAccelPheno -0.4327 0.5690 -1.5480 0.6826 0.447021
## DNAmAgeSkinBloodClockAdjAge -0.4221 0.4982 -1.3985 0.5543 0.396770
## AgeAccelGrim 0.0384 2.2578 -4.3869 4.4637 0.986441
## DNAmTLAdjAge -0.0182 0.0866 -0.1879 0.1515 0.833596
## IEAA -0.3244 0.6162 -1.5321 0.8833 0.598557
## EEAA -0.6694 0.6204 -1.8853 0.5465 0.280555
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum <= 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_ACE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0676 0.4095 -0.8703 0.7351 0.868924
## AgeAccelerationResidualHannum 0.5385 0.2996 -0.0487 1.1257 0.072276
## AgeAccelPheno 0.8656 0.4488 -0.0141 1.7454 0.053791
## DNAmAgeSkinBloodClockAdjAge -0.0527 0.2899 -0.6209 0.5155 0.855779
## AgeAccelGrim 0.3452 1.2220 -2.0499 2.7403 0.777573
## DNAmTLAdjAge -0.0323 0.2496 -0.5215 0.4568 0.896935
## IEAA 0.2100 0.4170 -0.6073 1.0274 0.614479
## EEAA 0.6115 0.4470 -0.2647 1.4877 0.171350
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_FLU_PHE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2294 0.6695 -1.0829 1.5417 0.731885
## AgeAccelerationResidualHannum 1.1023 0.5570 0.0105 2.1940 0.047825
## AgeAccelPheno 0.0378 0.5802 -1.0993 1.1750 0.947994
## DNAmAgeSkinBloodClockAdjAge -0.1599 0.4469 -1.0359 0.7161 0.720579
## AgeAccelGrim 0.3005 3.1494 -5.8723 6.4732 0.923993
## DNAmTLAdjAge -0.0282 0.1764 -0.3740 0.3176 0.872953
## IEAA 0.6668 0.6129 -0.5345 1.8681 0.276640
## EEAA 0.6630 0.7147 -0.7379 2.0639 0.353604
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum <= 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_PYR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1678 0.5190 -1.1850 0.8493 0.746403
## AgeAccelerationResidualHannum 0.1705 0.3396 -0.4952 0.8362 0.615615
## AgeAccelPheno 1.0643 0.4809 0.1218 2.0068 0.026885
## DNAmAgeSkinBloodClockAdjAge 0.8517 0.4134 0.0415 1.6620 0.039370
## AgeAccelGrim 0.2722 1.0787 -1.8422 2.3865 0.800815
## DNAmTLAdjAge 0.0145 0.0616 -0.1061 0.1352 0.813522
## IEAA -0.2626 0.5462 -1.3332 0.8080 0.630645
## EEAA 0.3783 0.4613 -0.5258 1.2824 0.412108
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_CHR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0358 0.4716 -0.9601 0.8885 0.939485
## AgeAccelerationResidualHannum 0.0231 0.4494 -0.8577 0.9039 0.959029
## AgeAccelPheno -0.0641 0.3640 -0.7775 0.6492 0.860195
## DNAmAgeSkinBloodClockAdjAge 0.1292 0.3622 -0.5807 0.8390 0.721356
## AgeAccelGrim 0.2892 0.3421 -0.3813 0.9597 0.397891
## DNAmTLAdjAge -0.0001 0.0320 -0.0630 0.0627 0.996417
## IEAA -0.3694 0.4209 -1.1944 0.4556 0.380201
## EEAA 0.1927 0.5040 -0.7951 1.1806 0.702134
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
Sensitivity analyses
GEE (No confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## The estimated effects:
## $URI5_NAP_1M_2M
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1473 0.4460 -0.7268 1.0214 0.741180
## AgeAccelerationResidualHannum -0.4240 0.4146 -1.2367 0.3886 0.306475
## AgeAccelPheno -0.1006 0.4182 -0.9202 0.7190 0.809962
## DNAmAgeSkinBloodClockAdjAge -0.2657 0.3673 -0.9856 0.4543 0.469515
## AgeAccelGrim 0.6108 0.5236 -0.4155 1.6372 0.243400
## DNAmTLAdjAge -0.0208 0.0150 -0.0501 0.0085 0.164974
## IEAA -0.0905 0.4819 -1.0350 0.8541 0.851117
## EEAA -0.1229 0.4975 -1.0980 0.8523 0.804896
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_ACE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2285 0.4993 -1.2072 0.7502 0.647284
## AgeAccelerationResidualHannum 0.4198 0.3081 -0.1841 1.0237 0.173068
## AgeAccelPheno 0.6744 0.4293 -0.1670 1.5158 0.116208
## DNAmAgeSkinBloodClockAdjAge -0.0801 0.3082 -0.6842 0.5240 0.794906
## AgeAccelGrim 0.6990 0.5178 -0.3160 1.7139 0.177097
## DNAmTLAdjAge -0.0116 0.0111 -0.0333 0.0100 0.292805
## IEAA 0.0690 0.4065 -0.7279 0.8658 0.865303
## EEAA 0.4841 0.4111 -0.3216 1.2899 0.238933
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_FLU_PHE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1805 0.3991 -0.9628 0.6018 0.651116
## AgeAccelerationResidualHannum 0.1986 0.3957 -0.5770 0.9742 0.615824
## AgeAccelPheno 0.3648 0.3897 -0.3991 1.1287 0.349240
## DNAmAgeSkinBloodClockAdjAge 0.0178 0.3059 -0.5817 0.6173 0.953491
## AgeAccelGrim 0.6512 0.4204 -0.1729 1.4753 0.121428
## DNAmTLAdjAge -0.0379 0.0132 -0.0638 -0.0120 0.004106
## IEAA 0.0781 0.4215 -0.7480 0.9043 0.852969
## EEAA 0.3989 0.5261 -0.6322 1.4300 0.448246
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge <= 0.01
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_PYR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.6944 0.5390 -1.7510 0.3621 0.197659
## AgeAccelerationResidualHannum 0.3103 0.3289 -0.3342 0.9549 0.345358
## AgeAccelPheno 0.8057 0.3842 0.0527 1.5587 0.035981
## DNAmAgeSkinBloodClockAdjAge 0.5169 0.3580 -0.1847 1.2185 0.148702
## AgeAccelGrim 0.3848 0.2591 -0.1230 0.8925 0.137480
## DNAmTLAdjAge -0.0220 0.0160 -0.0534 0.0094 0.168903
## IEAA -0.3043 0.3924 -1.0733 0.4647 0.438016
## EEAA 0.3575 0.4270 -0.4795 1.1945 0.402519
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_CHR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0110 0.3500 -0.6971 0.6750 0.974841
## AgeAccelerationResidualHannum 0.0726 0.3971 -0.7056 0.8509 0.854827
## AgeAccelPheno -0.0275 0.3806 -0.7734 0.7185 0.942468
## DNAmAgeSkinBloodClockAdjAge 0.0540 0.2893 -0.5130 0.6210 0.851836
## AgeAccelGrim 0.4234 0.2654 -0.0968 0.9436 0.110640
## DNAmTLAdjAge -0.0259 0.0204 -0.0658 0.0141 0.204642
## IEAA -0.2246 0.3759 -0.9613 0.5121 0.550163
## EEAA 0.2037 0.4954 -0.7673 1.1748 0.680898
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (No confounders, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * NAP\_1M\_2M + \beta_2 ACE + \beta_3
FLU\_PHE + \beta_4 PYR + \beta_5 CHR \\
& + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## [1] "Fitting with 104 observations."
## The estimated effects:
## $URI5_NAP_1M_2M
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.5575 0.4986 -0.4197 1.5347 0.263513
## AgeAccelerationResidualHannum -1.2574 0.4912 -2.2201 -0.2948 0.010464
## AgeAccelPheno -0.6312 0.4959 -1.6033 0.3408 0.203075
## DNAmAgeSkinBloodClockAdjAge -0.4482 0.4231 -1.2776 0.3811 0.289459
## AgeAccelGrim 0.0269 0.4878 -0.9291 0.9830 0.955997
## DNAmTLAdjAge 0.0085 0.0239 -0.0383 0.0554 0.721152
## IEAA -0.2575 0.5535 -1.3423 0.8273 0.641770
## EEAA -0.8048 0.6053 -1.9911 0.3815 0.183639
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum <= 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_ACE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1791 0.4358 -1.0334 0.6751 0.681045
## AgeAccelerationResidualHannum 0.5422 0.2967 -0.0394 1.1238 0.067678
## AgeAccelPheno 0.6104 0.4294 -0.2311 1.4519 0.155116
## DNAmAgeSkinBloodClockAdjAge -0.0949 0.2930 -0.6692 0.4794 0.746047
## AgeAccelGrim 0.4779 0.4078 -0.3214 1.2772 0.241236
## DNAmTLAdjAge -0.0184 0.0171 -0.0520 0.0152 0.283818
## IEAA 0.0965 0.4074 -0.7019 0.8949 0.812796
## EEAA 0.5412 0.4174 -0.2768 1.3592 0.194743
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_FLU_PHE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.5210 0.7178 -1.9278 0.8859 0.467962
## AgeAccelerationResidualHannum 1.2377 0.5416 0.1763 2.2992 0.022283
## AgeAccelPheno 0.5069 0.5833 -0.6364 1.6503 0.384847
## DNAmAgeSkinBloodClockAdjAge -0.1016 0.3721 -0.8309 0.6276 0.784788
## AgeAccelGrim 0.3359 0.5729 -0.7870 1.4588 0.557676
## DNAmTLAdjAge -0.0568 0.0385 -0.1323 0.0186 0.139901
## IEAA 0.7060 0.5943 -0.4588 1.8707 0.234840
## EEAA 0.8738 0.6981 -0.4945 2.2420 0.210707
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum <= 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_PYR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.4542 0.5151 -1.4639 0.5554 0.377907
## AgeAccelerationResidualHannum 0.0510 0.3262 -0.5883 0.6902 0.875848
## AgeAccelPheno 0.7396 0.4357 -0.1145 1.5937 0.089638
## DNAmAgeSkinBloodClockAdjAge 0.7389 0.3875 -0.0206 1.4985 0.056549
## AgeAccelGrim 0.0822 0.2914 -0.4890 0.6534 0.777897
## DNAmTLAdjAge 0.0100 0.0901 -0.1665 0.1865 0.911729
## IEAA -0.5378 0.5098 -1.5369 0.4614 0.291448
## EEAA 0.0842 0.4365 -0.7714 0.9398 0.847085
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_CHR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1991 0.6655 -1.1053 1.5034 0.764844
## AgeAccelerationResidualHannum -0.0389 0.4426 -0.9064 0.8285 0.929902
## AgeAccelPheno -0.2256 0.3955 -1.0008 0.5497 0.568467
## DNAmAgeSkinBloodClockAdjAge 0.0815 0.3678 -0.6393 0.8023 0.824687
## AgeAccelGrim 0.2661 0.2066 -0.1388 0.6709 0.197732
## DNAmTLAdjAge 0.0082 0.0606 -0.1105 0.1269 0.892277
## IEAA -0.3609 0.4027 -1.1502 0.4285 0.370209
## EEAA 0.0579 0.5090 -0.9398 1.0556 0.909418
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
4.1. Current exposure to 5MC
Summary the exposure estimates:
|
Smokeles
(N=18) |
Smoky
(N=98) |
Wood_and_or_Plant
(N=13) |
Overall
(N=129) |
| cur_5mc |
|
|
|
|
| Mean (SD) |
4.15 (3.22) |
8.89 (4.27) |
7.57 (1.34) |
8.15 (4.23) |
| Median (IQR) |
2.68 (2.99) |
9.43 (4.17) |
7.40 (0) |
8.26 (4.48) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
Primary analysis
GEE (with confounders)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *cur\_5mc\\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.043405227 0.115493287924362 -0.18296162
## AgeAccelerationResidualHannum -0.027158185 0.0999991181792323 -0.22315646
## AgeAccelPheno 0.118926749 0.100656276642595 -0.07835955
## DNAmAgeSkinBloodClockAdjAge 0.116943084 0.0716231975184864 -0.02343838
## AgeAccelGrim 0.148955554 0.0506687424790981 0.04964482
## DNAmTLAdjAge -0.004557601 0.00327977322802548 -0.01098596
## IEAA 0.020150560 0.123949043708331 -0.22278957
## EEAA -0.042628589 0.132789328425354 -0.30289567
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.269772072 0.707047242657413 > 0.05
## AgeAccelerationResidualHannum 0.168840087 0.785941715190563 > 0.05
## AgeAccelPheno 0.316213051 0.237398797561661 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.257324551 0.102520682085509 > 0.05
## AgeAccelGrim 0.248266290 0.00328432839999115 <= 0.01
## DNAmTLAdjAge 0.001870755 0.164647803635041 > 0.05
## IEAA 0.263090685 0.870855969945509 > 0.05
## EEAA 0.217638494 0.748192043476084 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
Sensitivity analysis
GEE (no confounders)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *cur\_5mc + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.044863339 0.113864633110002 -0.17831134
## AgeAccelerationResidualHannum -0.017041115 0.0978064100987568 -0.20874168
## AgeAccelPheno 0.107159703 0.100283750609095 -0.08939645
## DNAmAgeSkinBloodClockAdjAge 0.106144306 0.0713739765678327 -0.03374869
## AgeAccelGrim 0.140523649 0.0550039747044841 0.03271586
## DNAmTLAdjAge -0.004200799 0.00335993883966329 -0.01078628
## IEAA 0.016148695 0.121516907890369 -0.22202444
## EEAA -0.025686042 0.127591566498255 -0.27576551
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.268038020 0.693576669249562 > 0.05
## AgeAccelerationResidualHannum 0.174659449 0.86168226954879 > 0.05
## AgeAccelPheno 0.303715854 0.285265741749724 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.246037300 0.136973360441059 > 0.05
## AgeAccelGrim 0.248331440 0.0106251631971735 <= 0.05
## DNAmTLAdjAge 0.002384681 0.211204364462331 > 0.05
## IEAA 0.254321835 0.894278337455743 > 0.05
## EEAA 0.224393428 0.840452605508899 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
4.2. Childhood exposure to 5MC
Summary the exposure estimates:
|
Smokeles
(N=18) |
Smoky
(N=98) |
Wood_and_or_Plant
(N=13) |
Overall
(N=129) |
| bir_5mc |
|
|
|
|
| Mean (SD) |
3.38 (2.38) |
5.44 (2.80) |
5.84 (2.77) |
5.22 (2.82) |
| Median (IQR) |
2.33 (3.31) |
4.83 (5.33) |
3.70 (5.09) |
4.75 (5.34) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
Primary analysis
GEE (with confounders)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *childhood\_5mc\\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.013581238 0.1525495208842 -0.285415823
## AgeAccelerationResidualHannum 0.135320612 0.143152618792748 -0.145258521
## AgeAccelPheno 0.288191817 0.150828508622891 -0.007432059
## DNAmAgeSkinBloodClockAdjAge 0.156895355 0.112196416684544 -0.063009621
## AgeAccelGrim 0.299554330 0.0856477119470605 0.131684815
## DNAmTLAdjAge -0.001670684 0.00638501299772504 -0.014185309
## IEAA -0.134530357 0.14459026100462 -0.417927269
## EEAA 0.222801828 0.182782972150885 -0.135452798
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.31257830 0.929059351419451 > 0.05
## AgeAccelerationResidualHannum 0.41589974 0.344511294913712 > 0.05
## AgeAccelPheno 0.58381569 0.056039917225708 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.37680033 0.161993287747863 > 0.05
## AgeAccelGrim 0.46742385 0.000469610751128058 <= 0.001
## DNAmTLAdjAge 0.01084394 0.793585821402502 > 0.05
## IEAA 0.14886655 0.352151209489498 > 0.05
## EEAA 0.58105645 0.222866236215159 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
Sensitivity analysis
GEE (no confounders)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *childhood\_5mc + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.044863339 0.113864633110002 -0.17831134
## AgeAccelerationResidualHannum -0.017041115 0.0978064100987568 -0.20874168
## AgeAccelPheno 0.107159703 0.100283750609095 -0.08939645
## DNAmAgeSkinBloodClockAdjAge 0.106144306 0.0713739765678327 -0.03374869
## AgeAccelGrim 0.140523649 0.0550039747044841 0.03271586
## DNAmTLAdjAge -0.004200799 0.00335993883966329 -0.01078628
## IEAA 0.016148695 0.121516907890369 -0.22202444
## EEAA -0.025686042 0.127591566498255 -0.27576551
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.268038020 0.693576669249562 > 0.05
## AgeAccelerationResidualHannum 0.174659449 0.86168226954879 > 0.05
## AgeAccelPheno 0.303715854 0.285265741749724 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.246037300 0.136973360441059 > 0.05
## AgeAccelGrim 0.248331440 0.0106251631971735 <= 0.05
## DNAmTLAdjAge 0.002384681 0.211204364462331 > 0.05
## IEAA 0.254321835 0.894278337455743 > 0.05
## EEAA 0.224393428 0.840452605508899 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
4.3. Cumulative exposure to 5MC
Summary the exposure estimates:
|
Smokeles
(N=18) |
Smoky
(N=98) |
Wood_and_or_Plant
(N=13) |
Overall
(N=129) |
| cum_5mc |
|
|
|
|
| Mean (SD) |
158 (123) |
289 (148) |
316 (135) |
275 (150) |
| Median (IQR) |
110 (130) |
270 (212) |
315 (135) |
257 (209) |
| Missing |
2 (11.1%) |
1 (1.0%) |
0 (0%) |
3 (2.3%) |
Primary analysis
GEE (with confounders)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *cum\_5mc\\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.0011297683 0.00321754110538496 -0.0051766123
## AgeAccelerationResidualHannum 0.0024297203 0.00286859328247939 -0.0031927225
## AgeAccelPheno 0.0046561822 0.00315660422742595 -0.0015307621
## DNAmAgeSkinBloodClockAdjAge 0.0022230277 0.00243998088414274 -0.0025593349
## AgeAccelGrim 0.0058800703 0.00161826592950822 0.0027082691
## DNAmTLAdjAge -0.0001174706 0.000128468642577699 -0.0003692691
## IEAA -0.0007651314 0.00302849757225974 -0.0067009866
## EEAA 0.0042085613 0.00367291833078406 -0.0029903586
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.0074361488 0.725492440526316 > 0.05
## AgeAccelerationResidualHannum 0.0080521632 0.396990881252127 > 0.05
## AgeAccelPheno 0.0108431264 0.140196283843566 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.0070053902 0.362251089952007 > 0.05
## AgeAccelGrim 0.0090518716 0.000279534738611642 <= 0.001
## DNAmTLAdjAge 0.0001343279 0.360511350051069 > 0.05
## IEAA 0.0051707239 0.8005434220148 > 0.05
## EEAA 0.0114074812 0.251863102495362 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
Sensitivity analysis
GEE (no confounders)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *cum\_5mc + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] "Fitting with 126 observations."
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual -0.0009917356 0.0027245425973913 -0.0063318390
## AgeAccelerationResidualHannum 0.0024968640 0.00247936234502612 -0.0023626862
## AgeAccelPheno 0.0047965293 0.00266935100058998 -0.0004353987
## DNAmAgeSkinBloodClockAdjAge 0.0013096169 0.00207570564676486 -0.0027587661
## AgeAccelGrim 0.0047733361 0.00163901122252633 0.0015608741
## DNAmTLAdjAge -0.0001203660 0.000108228672285064 -0.0003324941
## IEAA -0.0015487572 0.00257420977692438 -0.0065942084
## EEAA 0.0035090514 0.00318009041422209 -0.0027239258
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.00434836794 0.715857447596787 > 0.05
## AgeAccelerationResidualHannum 0.00735641417 0.313906458923568 > 0.05
## AgeAccelPheno 0.01002845722 0.0723531358161104 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.00537799999 0.528088827964163 > 0.05
## AgeAccelGrim 0.00798579808 0.00358747245035784 <= 0.01
## DNAmTLAdjAge 0.00009176225 0.266075912628183 > 0.05
## IEAA 0.00349669395 0.547411302915121 > 0.05
## EEAA 0.00974202860 0.269834439221523 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
4.0. All plots together
